This study enhances the ship domain model using an N-focal ellipse to improve risk assessment for maritime autonomous surface ships (MASS). It distinguishes between collision-avoidance tasks involving moving objects under COLREGs and other navigational hazards by categorizing foci into vessel dimensions, kinematic parameters, and maneuvering characteristics. The model introduces multipliers to adjust domain boundaries based on navigation conditions and defines a minimum navigational safety margin. Simulations support the findings, suggesting the model could advance MASS navigational safety when integrated into control systems.
This study redefines the "route" concept for Maritime Autonomous Surface Ships (MASS) by expanding the IEC 61174 RTZ format to include turn arcs, corridors, speed profiles, and time stamps for dynamic feasibility. Using the MMG maneuvering and Nomoto models, the proposed digital route improved trajectory accuracy, reducing maximum deviation from 35.3 m to 11.3 m and rudder angles from 25.1° to 17.6° in tests with a 100 m vessel. The authors introduce an "acceptable solution" hypothesis for tactical replanning and a four-level planning architecture. Future work may explore advanced simulations, deceleration modes, and cooperative planning for multiple MASS.
This paper introduces a BIT*-based path-planning method for Maritime Autonomous Surface Ships (MASS) to handle dynamic obstacle uncertainties in congested waters. The approach combines collision avoidance and path replanning into a single optimization problem, using probabilistic collision risk in the BIT* cost function to prioritize safer routes. It also integrates COLREGs rules to ensure compliance with maritime navigation regulations. Simulations show the method produces safe and reliable paths despite positional uncertainty from sensor errors and communication delays.
This paper proposes a neural network-based method for cooperative collision avoidance in Maritime Autonomous Surface Ships (MASS). The system uses vessel coordinates and velocity components as inputs and outputs course adjustments to maintain a safe distance. The neural network has eight hidden layers with 1,000 neurons each and was trained on one million samples using a two-iteration gradient optimization process. After training, the model achieved a mean prediction error of about 2°, though maximum errors remained high at several tens of degrees.
This paper introduces a decoupled framework for COLREGs-compliant autonomous ship navigation in coastal waters, separating global path planning from local collision avoidance. The global layer uses Adaptive Angular-Domain Potential Field (AAPF) for path generation and Farsighted Sweep Trajectory Optimization (FSTO) for refinement, while the local layer employs Environment-Adaptive Dynamic Window Approach (EA-DWA) for real-time risk assessment and avoidance. Simulations demonstrate reduced computational burden and improved path usability, though performance depends on environment-specific parameter adjustments. The approach ensures compliance with maritime collision regulations while balancing efficiency and adaptability.
This study introduces an autonomous ship navigation method using the OODA (Observe–Orient–Decide–Act) loop theory to improve safety and decision-making in complex maritime scenarios. The approach includes dynamic ship domain modeling, risk assessment, and collision avoidance strategies based on a three-degree-of-freedom motion model and an enhanced velocity obstacle algorithm. Simulations on OpenCPN confirmed the method’s effectiveness, maintaining safe distances and compliance with COLREGs (Collision Regulations) even in high-risk encounters. The framework provides a closed-loop system for real-world autonomous navigation applications.
This paper reviews ship path planning for autonomous navigation, highlighting its complexity due to collision risks, efficiency, COLREGs compliance, and environmental factors. It compares model-driven methods (search-based, optimization) with deep reinforcement learning approaches, focusing on decision-making, safety, and multi-vessel interactions. Key challenges include rule formalization, partial observability, benchmark inconsistencies, and limited real-world validation. Future priorities involve safety constraints, digital twins, meta-learning, and scalable multi-agent coordination for reliable deployment.
This paper addresses gaps in the International Regulations for Preventing Collisions at Sea (COLREG) for Maritime Autonomous Surface Ships (MASS) by incorporating relative speed variations between vessels, expanding on prior work limited to identical-speed scenarios. It adapts collision-risk geometry calculations, such as collision angle, to assess how speed differences affect navigation safety. The study refines a geometric optimization method to efficiently analyze two-dimensional encounter scenarios, maintaining near-polynomial search complexity. The approach identifies high-risk vessel positions in under half a second, making it viable for onboard navigation warning systems.
This study presents a control system for maritime autonomous surface ships (MASSs) that integrates human supervision into platoon navigation while adhering to COLREGs collision avoidance rules. A virtual leader allows human operators to adjust the platoon’s speed, while a finite state machine identifies encounter scenarios and applies safety constraints via control barrier functions. The system uses a cooperative path-following guidance law and quadratic optimization to balance collision avoidance with platoon coordination. Simulations confirm the approach effectively maintains formation and complies with maritime safety regulations.
This paper presents a modernized three-level hierarchical control system for Maritime Autonomous Surface Ships (MASS) to handle external disturbances like waves, wind, and currents. It proposes a modified proportional-derivative control law with corrections for roll, trim, and feedforward compensation, improving course accuracy and reliability in harsh conditions. The system integrates sensor data, processing, and control levels while monitoring seakeeping parameters without extra sensors. Its scalable and open structure allows integration with vessel traffic and motion control systems, enhancing navigation safety.
The paper critiques the assumption that Maritime Autonomous Surface Ships (MASS) and port digitalization alone solve maritime transport challenges, arguing risks arise from systemic factors like vessel size, infrastructure limits, and organizational complexity. It emphasizes MASS as part of a broader socio-technical system, requiring smart ports, traffic management, and maritime simulators for safe coexistence with manned vessels. The role of simulators as cyber-physical test beds for validating operations, decision-support systems, and human-automation interaction is highlighted. The authors stress that successful MASS integration depends on managing complexity through predictive coordination, simulation-based validation, and regulatory adaptations, rather than just increasing autonomy.
This study evaluates berth locations at Kaohsiung Port for mooring Maritime Autonomous Surface Ships (MASS) by combining expert input with a two-step method: the qualitative Analytic Hierarchy Process (AHP) and quantitative Automatic Identification System (AIS). Researchers identified three key assessment aspects, nine criteria, and three potential berth alternatives. The analysis determined that the 7th Container Centre is the most suitable berth for MASS mooring. Findings aim to guide port authorities and operators in transitioning to intelligent port infrastructure.
The paper introduces PSN-PPO, a deep reinforcement learning method for autonomous navigation of unmanned surface vehicles (USVs) that separates navigation and obstacle avoidance into two jointly trained policy branches. The approach uses a navigation branch for course guidance and an avoidance branch for bounded corrections, modulated by a learnable risk gate. Simulations across five scenarios—including obstacle-free navigation, static obstacles, and dynamic encounters—show PSN-PPO improves efficiency and collision avoidance compared to standard PPO. The method was tested using a three-degree-of-freedom vessel motion model in a closed-loop framework.
This paper reviews the evolution and current state of ship route planning algorithms for Maritime Autonomous Surface Ships (MASS), highlighting human error as a key cause of maritime accidents. It categorizes algorithms into three groups: baseline trajectory methods, environment-adaptive methods, and multi-objective intelligent decision-making methods, analyzing their applications, limitations, and COLREGs compliance. The study identifies gaps in existing algorithms, suggesting future trends include integrating multiple approaches, interdisciplinary collaboration, and addressing uncertainty. Refining COLREGs integration is noted as critical for advancing autonomous shipping and global trade.
This study introduces an energy field model for assessing navigation risks in Maritime Autonomous Surface Ships (MASS), focusing on multi-ship interactions. It quantifies maritime traffic complexity using ship attributes and the Potential Risk Ship Domain (PRSD) framework, then develops a model to capture dynamic interactions between ships. The approach classifies navigation scenarios using the ALARP principle, enabling adaptive operation mode switching. Results show the model effectively identifies risk patterns in high-density traffic, improving MASS safety in complex environments.
This study explores the importance of smart marine aids to navigation (AtoN) for Maritime Autonomous Surface Ships (MASS) and assesses the economic impact of remote-operator training in Korea. It highlights the need for institutionalized training programs, as current education lags behind technological advancements. Using a market-opportunity framework, the research estimates potential economic benefits ranging from USD 606.8 million to USD 6.07 billion, depending on program length and market capture. The findings suggest that MASS training could enhance Korea’s maritime workforce and create a specialized education market.
This paper reviews research on autonomous ship and harbor maneuvering from 2015 to 2026, covering control systems, AI, sensor fusion, digital twins, and logistics. It introduces a seven-layer cyber-physical taxonomy and a maturity model to assess integration levels, from isolated vessel autonomy to fully federated systems. While progress has been made in individual areas like control and perception, gaps remain in cross-layer coordination, cybersecurity, and regulatory compliance. The authors propose a Federated Maritime Cyber-Physical Architecture to improve interoperability and resilience in autonomous harbor operations.
18openalex2026-07-31Y. Ding, W. Meng, S. He, Weiwei Li
This paper proposes a three-layer maritime IoT framework for autonomous ship collision avoidance, integrating vessel states, risk metrics, and environmental data to comply with COLREGs (International Regulations for Preventing Collisions at Sea). It uses a multi-agent PPO-LSTM algorithm with a centralised-training-decentralised-execution approach to handle partial observability and complex vessel interactions. Simulations in congested and narrow waterways show improved success rates, safer distances, and fewer rule violations compared to other methods. Explicit COLREGs-aware rewards and timely communication are key to its performance.
19아시아경제2026-07-09
Hanwha Systems launched a 30-ton unmanned surface vessel to advance AI-driven autonomous navigation technology. The vessel is designed to operate without human intervention, enhancing maritime capabilities. This development aims to improve efficiency and safety in naval operations. The project reflects progress in integrating artificial intelligence into unmanned systems.
This study enhances autonomous ship navigation by improving collision avoidance in complex multi-ship encounters. It combines Velocity Obstacle (VO) and CPA/TCPA methods to assess collision risks and applies COLREGs rules to assign give-way and stand-on responsibilities. The proposed MVO-IDWA algorithm integrates these factors into the Dynamic Window Approach to optimize decision-making. Results show the method effectively considers ship responsibilities, risk levels, and movement trends for safe navigation.
VesselNav-Bench is an open benchmark for comparing autonomous ship navigation policies, using a shared 3-DOF simulator to ensure transparent and reproducible evaluations. Seven agents, including rule-based, optimization-based, and learning-based methods, were tested across nine scenarios under calm and disturbed conditions, with 360 episodes per agent. Classical and MPC approaches performed best in calm conditions, while MPC excelled under disturbances; safety shielding improved RL policy safety but revealed persistent generalization gaps. COLREGS Rule 17 compliance was limited to 0.76 across all agents, highlighting challenges for ASV certification. The benchmark, including code and logs, is publicly available.
This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve collision avoidance for autonomous ships by mimicking human navigator behavior. The system uses fuzzy clustering (FCM) and high-fidelity simulator data to train separate models for crossing, head-on, and overtaking scenarios, ensuring compliance with COLREGs and safe maneuvering. Validation shows smooth, collision-free trajectories with rudder commands resembling human actions. Sensitivity analysis reveals that different encounter types rely on distinct input variables, such as geometric factors for crossing and overtaking.
This review examines machine learning methods for autonomous vessel collision avoidance from 2021 to 2025, analyzing 445 studies after screening 832 records. Reinforcement learning, particularly Deep Reinforcement Learning (DRL), Proximal Policy Optimization (PPO), and Deep Q-Network (DQN), emerged as the leading approaches. The adoption of these techniques surged after 2022, reflecting their growing role in adaptive decision-making and trajectory prediction. The paper also outlines challenges in real-world deployment and future research directions.
24SOFREP2026-10-23
The U.S. Army plans to develop a fleet of up to 100 autonomous drone boats for the Pacific, following a successful August 2026 test of an uncrewed commercial ship in Hawaii. The test involved an 18-hour autonomous voyage with a full crew onboard, demonstrating navigation and vehicle loading/unloading capabilities. The Army aims to retrofit existing vessels with autonomy kits while also building purpose-built uncrewed boats, though regulatory and logistical challenges remain. The initiative is part of a broader effort to expand watercraft capacity after years of fleet reductions.
This study introduces a data-driven framework to improve collision-risk assessment for autonomous ships by combining Bayesian risk modeling with system identification-based manoeuvring predictions. It analyzes global maritime accident data using Bayesian Networks to identify key risk factors, then applies Dynamic Bayesian Networks to predict real-time collision risks during ship encounters using AIS data. The research also develops a non-parametric model to predict ship manoeuvring behavior, validated with KCS benchmark ship data, achieving accuracy comparable to deep learning with lower computational costs. The findings support enhanced collision risk evaluation and autonomous navigation through realistic trajectory predictions and control strategies.
This study introduces a fractal–episodic framework to analyze ship-control microvariability for navigation risk monitoring in Maritime Autonomous Surface Ships (MASS). The method processes AIS/ECDIS trajectory data using sliding-window analysis, fractal measures (Higuchi, DFA, Katz), and weighted aggregation to generate a chaos score and confidence index. It converts micro-motion irregularities into interpretable passage-level indicators for human-factor-aware risk assessment. The approach aims to enhance decision-support systems in autonomous maritime operations.
This paper examines the limitations of current autopilot systems in autonomous ships, which mainly handle basic navigation and lack strong collision avoidance. It proposes using VR simulators and model-scale vessel testing as a controlled "sandbox" environment to improve autonomous navigation. The study highlights the role of advanced predictors and AI in detecting and preventing close-encounter situations early. It also stresses the need to ensure both hardware and software reliability in AI systems to handle potential failures and maintain safety.
28Army Recognition2026-11-05
Leonardo and Shield AI signed an agreement on October 29, 2026, to integrate Shield AI’s Hivemind autonomy software into Leonardo’s Proteus uncrewed helicopter for the UK Royal Navy. The Proteus, a three-tonne aircraft developed under a £60 million UK Ministry of Defence programme, completed its maiden flight in January 2026 and is designed for anti-submarine warfare, ISR, and contested logistics missions. The Hivemind SDK will enable customized AI decision-making for these operations while Leonardo retains intellectual property rights. No integration timeline, test dates, or production orders have been disclosed.
This study compares a new deterministic path-planning algorithm, BMSSP, against Dijkstra’s and A* for autonomous surface vessels. BMSSP theoretically surpasses the sorting barrier with $O(m \log^{\frac{2}{3}} n)$ complexity and expands 29% fewer nodes than A* in simulations. While A* was fastest in CPU time (322 ms), BMSSP produced smoother trajectories with the lowest angular deviation (664.08°). Results suggest BMSSP improves navigation stability and energy efficiency in dynamic maritime environments.
This study introduces a Social-Cognitive Navigation Framework (SCNF) for autonomous shipping, using historical and real-time AIS data to enhance safety and efficiency. The framework combines a Recursive Hypergraph Transformer, Level-k reasoning, and a game-theoretic planner, outperforming existing methods in 500 trials. It achieved zero collisions, improved path efficiency, and reduced traffic disturbance in crossing and overtaking scenarios. While it suggests sustainability benefits, direct fuel and emissions reductions were not measured.
This paper introduces a predictive safety-control framework for autonomous surface vehicles (ASVs) that reconciles symmetric vessel dynamics with asymmetric collision-avoidance rules like COLREGs. A large language model (LLM) generates and validates structured COLREGs labels to guide asymmetric responsibilities, while a physical safety envelope, built on motion prediction and uncertainty inflation, ensures collision avoidance. A runtime assurance monitor checks semantic validity and solver performance to prevent unsafe decisions. Simulations show the approach improves rule-compliant navigation while reducing overly cautious maneuvers.
32safety4sea2026-10-29
South Korea plans to develop fully unmanned autonomous ship technology by 2032. The initiative aims to advance maritime automation and improve operational efficiency. The project reflects the country’s focus on innovation in the shipping industry. No specific methods or interim milestones were detailed in the announcement.
This paper introduces the Logic-Guided Interpretable Deep Reinforcement Learning (LG-IDRL) framework for autonomous ship collision avoidance, combining COLREGs-based rule reasoning with reinforcement learning (PPO). The system dynamically adjusts rewards and action constraints to ensure compliance with maritime rules while optimizing navigation. In tests, LG-IDRL outperformed a baseline, achieving a 92.9% success rate, 1.6% collision rate, and 98.7% rule compliance. Results are limited to predefined two-ship encounters and do not guarantee broader applicability.
This study introduces a framework for improving the safety of maritime autonomous surface ships (MASS) by analyzing perception uncertainty and optimizing sensor layouts. It models sensor detection probabilities, state-estimation uncertainties, and collision risks using an ellipse-based safety envelope. The optimized sensor configuration achieved 68.49% mean coverage, outperforming a fixed heuristic layout (67.89%), with daytime and nighttime coverage of 74.45% and 62.56%, respectively. A synthetic scenario with ten ships demonstrated the framework’s effectiveness in evaluating sensor performance under SOTIF constraints.
This paper presents a multi-sensor fusion system combining RGB camera and LiDAR data to improve obstacle detection and situational awareness for autonomous ships. Camera data is processed using a pre-trained object detection model, while LiDAR data undergoes clustering and classification via a Random Forest algorithm. The system tracks dynamic obstacles using a Global Nearest Neighbour algorithm with Kalman filtering, reducing false positives from noisy measurements. Real-world tests demonstrated 92% track number accuracy in complex maritime scenarios.
This paper introduces a decentralized motion planner for autonomous surface vessels in urban waterways, using a sampling-based model predictive control method. The planner simultaneously calculates the vessel's trajectory and predicts interactions with other vessels while adhering to traffic rules through discontinuous cost functions. Tested in real Amsterdam canal maps and outdoor experiments with scaled prototypes, the method achieved collision-free and deadlock-free navigation in 780 randomized trials, outperforming optimization-based and non-interaction-aware planners. Onboard execution ran at 10 Hz, successfully handling head-on and crossing scenarios.
Autonomous ships use algorithms, sensors, and AI for navigation, reducing human roles to remote supervision. This shift aims to improve efficiency, safety, and sustainability in maritime transport. However, it requires strict regulations to protect lives, cargo, and vessels. The paper explores AI-driven navigation, course correction, and challenges in safety and legal frameworks.
This paper examines challenges in autonomous ship navigation, such as emergency behavior, harsh conditions, and human error in mixed traffic. It explores how Digital Twins (DT), combined with AI and big data, could improve safety and efficiency in complex marine environments. The study reviews current autonomous ship systems and traffic models to assess DT’s potential for enhancing navigation. DT may also support decarbonization and smart mobility in maritime transport.
This study examines how combining autonomous ships with Just-in-Time (JIT) arrival systems could improve maritime logistics by reducing fuel use and emissions. Analyzing 882 voyages at Portugal’s Port of Sines, it found JIT with autonomous ships cut fuel consumption by 4.92% and greenhouse gas emissions by 4.90%, while improving arrival punctuality by about 1.5 hours. Early arrivals saw the biggest benefits, with fuel use dropping 36.86% and emissions by over 3,180 tCO₂, but late arrivals faced higher fuel costs. Successful implementation requires real-time data, digital infrastructure, and stakeholder collaboration.
This paper presents a ship–shore cooperative experimental platform for unmanned surface vehicles (USVs) to bridge the gap between simulation and real-world deployment. The system includes a shore control station, portable terminal, and onboard components with multimodal sensing, redundant communication (private radio, 4G/5G, and short-range RC), and hardware arbitration. A control-authority model ensures fail-safe transitions between autonomous and human control, while LiDAR-based obstacle detection enables collision avoidance. Field tests demonstrated autonomous navigation, human intervention, and data collection across 18.26 km of trajectories in four water environments.
This paper introduces LDA-D3QN, an improved deep reinforcement learning method for autonomous navigation of unmanned surface vehicles (USVs) in complex obstacle scenarios. The method reduces redundant environmental data by combining target-related and local obstacle features, improving decision stability and training efficiency. Tested on a simulation platform, LDA-D3QN achieved a 91.4% success rate, outperforming other reinforcement learning algorithms like PPO and Dueling DQN. Preliminary real-world sensor data validation supports its feasibility for future autonomous navigation applications.
The paper presents the Low-Observable Deployable Modular Surface Platform (LODMSP) as a future concept for autonomous maritime systems, moving beyond fixed-deck modularity. It proposes that next-generation platforms should compress into low-profile transit forms and expand for mission-specific interfaces. Analytical metrics like deployment ratio and modular separation index are introduced to evaluate such designs. The concept is framed within broader trends in unmanned vessels and distributed maritime operations but is not a procurement or engineering proposal.
This paper introduces a framework to identify and prioritize critical safety factors during transitions between manual and autonomous navigation in marine vessels. Using System-Theoretic Process Analysis (STPA), the authors analyze hazards, model control structures, and pinpoint unsafe actions like delayed transitions or alarm failures. The method was applied to an autopilot system between two ports, leveraging maritime incident reports to uncover risks. Findings aim to guide safety improvements for Maritime Autonomous Surface Ships (MASS) in mixed fleets.
44nippon.com2026-04-23
A Japanese consortium is developing autonomous maritime shipping technology to enable crewless vessels. The initiative aims to address labor shortages and improve efficiency in the shipping industry. Testing and development are underway, with a focus on leading global advancements in autonomous navigation. The project involves collaboration among major Japanese companies and research institutions.
45The Nation Newspaper2026-11-06
Nigerian President Tinubu unveiled the ADX-Barra 200, an autonomous vessel developed by Arridex Group, on November 6, 2026. The event marks the introduction of the country’s first locally designed unmanned maritime vehicle. The vessel is expected to enhance maritime operations, though specific applications were not detailed. The launch reflects efforts to advance Nigeria’s technological capabilities in the maritime sector.
46Business Wire2026-11-05Naval News
Fairbanks Morse Defense introduced AutoHook, a system for fully autonomous vessel recovery, at EURONAVAL 2026. The technology aims to enable unmanned ships to perform recovery operations without human intervention. The unveiling took place on November 5, 2026. No specific technical details or performance results were disclosed in the announcement.
47WorkBoat2026-11-03
Bulwark Dynamics, a California-based autonomous vessel company, secured $6.8 million in seed funding to expand production and development. The company partnered with Japan’s Onomichi Dockyard to manufacture its Caravel autonomous landing craft, with plans to establish a dedicated production site in Japan. The Caravel is designed for unmanned cargo transport, capable of autonomous transit, beach landings, and offloading in GPS-denied environments. Bulwark aims to serve military logistics and civilian disaster relief, with 15', 35', and 55' variants in development.
48MarineLink2026-11-02
Fairbanks Morse Defense introduced an Autonomous Vessel Recovery System on November 2, 2026. The system is designed to enhance naval operations by enabling unmanned recovery of vessels. Details on specific capabilities or deployment plans were not provided in the announcement. The technology aims to improve efficiency and safety in maritime defense.
49Marine Log2026-11-02
Fairbanks Morse Defense (FMD) introduced AutoHook, a fully-autonomous vessel recovery interface, at EURONAVAL 2026 in Paris. The system was developed by FMD’s Norway-based division and is the first of its kind. AutoHook aims to enable autonomous recovery operations for naval vessels. The technology was unveiled on November 2, 2026.
50oceannews2026-11-02
Fairbanks Morse Defense unveiled AutoHook™ at EURONAVAL 2026, a fully autonomous vessel recovery system for uncrewed surface vessels (USVs) developed by Vestdavit. The system operates without human intervention, achieving reliable performance in Sea State 4 conditions—surpassing current solutions limited to Sea State 2. AutoHook eliminates manual connections, reducing injury risks, and integrates easily with existing vessels due to its compact, clamp-on design. Designed for NATO-aligned interoperability, it supports allied fleets with real-time tracking and autonomous recovery in high-risk maritime environments.
This paper presents a COLREGs-compliant trajectory planning framework for autonomous ships that unifies static and dynamic obstacle avoidance by parameterizing time in space. It converts dynamic obstacles into polygonal representations, integrating global and local planners while accounting for uncertainties in vessel speed and course. The method is implemented using an extended version of the SeaCharts Python library, which now supports S-57 files and weather data. Case studies demonstrate improved robustness against target ship motion uncertainty compared to velocity obstacle and scenario-based MPC approaches.
52Yonhap News Agency2026-10-19
HD Hyundai and Anduril completed a successful test of autonomous vessel technology in the U.S. The trial demonstrated the capability of unmanned maritime systems. The collaboration focused on advancing navigation and operational autonomy for ships. No further details on specific outcomes or applications were provided.
53조선일보2026-10-19
HD Hyundai and Anduril successfully demonstrated autonomous vessel technology for a 368-ton ship. The collaboration showcased advanced navigation and operational capabilities without human intervention. The test highlights progress in maritime autonomy for large-scale vessels. No specific technical details or performance metrics were disclosed.
This study introduces a method to identify high-risk ships in mixed maritime traffic involving autonomous (MASS) and conventional vessels. It uses a 4D-OPTICS approach to filter ships in dense encounters and an Axiomatic Fuzzy Set (AFS) model to convert risk indicators into interpretable semantic categories like spatial proximity and motion uncertainty. A hierarchical model then prioritizes ships based on COLREGs rules and semantic risk levels. Simulations and real-world AIS data from Yingkou Port validate the framework’s effectiveness.
55TNGlobal2026-09-22
Clear Robotics and ReSustainability launched an electric, semi-autonomous vessel on a Singapore river in September 2026. The project aims to demonstrate sustainable and efficient waterway operations. The vessel combines automation with zero-emission technology for urban logistics and environmental monitoring. This deployment marks a step toward modernizing river transport in Singapore.
This paper details a real-time obstacle avoidance system for autonomous vessels, tested in a 2D maritime simulation. The system uses a proximity detection algorithm built with Pygame to adjust ship trajectories and prevent collisions with static or moving obstacles. It incorporates a ship motion model and particle system for realistic environmental representation. The approach aims to support autonomous navigation, telemetry control, and intelligent maritime transport solutions.
This paper presents a curriculum-guided reinforcement learning framework for IoT-enabled autonomous maritime navigation in smart ports. The approach uses a shared recurrent policy to improve decision-making under partial observability and dense traffic, with centralized offline training but fully onboard execution. Simulations in realistic port environments show improved reliability, collision avoidance, and training stability over baseline methods. The method also generalizes well to unseen high-density scenarios, offering a scalable solution for smart port operations.
This review examines recent advancements in path planning for Autonomous Surface Vehicles (ASVs), highlighting five key issues: navigation environment, environment modeling, ship motion models, collision avoidance, and optimization. It categorizes existing algorithms into graph search-based, sampling-based, numerical optimization-based, and AI-based methods, detailing their improvements and applications. Traditional algorithms excel in static environments but struggle with dynamic constraints, while AI-based approaches, particularly deep reinforcement learning, show superior adaptability and multi-agent coordination. The paper identifies current challenges and outlines future research directions for safer and more optimized ASV navigation.
The study introduces a Time-Dynamic Theta* (TDM-Theta*) algorithm to improve autonomous ship route planning in complex and extreme sea conditions. The method combines traditional any-angle path planning with a temporal dimension and incorporates real-time significant wave height as a dynamic constraint. Simulation tests across nine cases showed the algorithm efficiently generates optimal paths in static terrains and achieves 100% proactive risk avoidance in extreme weather scenarios with multiple typhoons, with minimal computational overhead. The research supports real-time safe route decision-making for intelligent ships in volatile maritime environments.
This paper introduces algorithms for automatic collision avoidance in multi-ship scenarios by identifying risk-free navigation areas and compliant maneuvers. Using AIS data, safety parameters, and COLREG rules, the algorithms replicate human decision-making processes of navigational officers. Polar graphs and weighted velocity areas help determine optimal maneuvers while adhering to maritime regulations. The output is designed for integration into autonomous navigation systems.
This paper introduces an adaptive mission planning method for solar-powered autonomous surface vessels (ASVs) to optimize scientific data collection. The approach uses a clarity-driven ergodic search, where clarity is a mathematically defined measure tied to information entropy, to prioritize high-interest areas identified during the mission. The system continuously updates its path based on real-time measurements and energy constraints. Simulations and field experiments at Jordan Lake, North Carolina, validate the method’s effectiveness.
This paper provides frozen reproduction materials for a synthetic study on mixed autonomous and human-driven ship traffic. It includes code, numerical data, reference scenarios, and instructions for independent verification. The results are not based on real-world AIS or maritime traffic data and should not be considered certified for navigation safety. The archive uses open licenses (Apache-2.0, CC BY 4.0) for author-owned content, with upstream MIT/BSD terms for third-party software, and restricts checkpoint deserialization to trusted local files.
63Container News2026-08-25
Greek companies have formed a partnership to develop an autonomous surface vessel. The collaboration aims to advance maritime technology in Greece. No specific details on the companies involved or project timeline were provided. The initiative focuses on innovation in autonomous shipping.
64Ocean News & Technology2026-07-22
A 2026 study compares the navigation performance of experienced ship captains with AI-driven autonomous systems. The article explores how AI technologies are being tested to match or surpass human decision-making in complex maritime environments. It highlights ongoing trials and regulatory challenges in adopting fully autonomous ships. The discussion focuses on safety, efficiency, and the future of crewed versus uncrewed vessels.
This paper presents a real-time telemetry system for Maritime Autonomous Surface Ships (MASS) that collects sensor data via a Raspberry Pi 5, stores it in a PostgreSQL database, and visualizes it through a web application. It also develops an AI-based speed controller using the Soft Actor-Critic (SAC) reinforcement learning algorithm. The controller relies on an XGBoost model trained on Simulink vessel dynamics data to issue throttle commands. The system successfully tracks multiple speed profiles and serves as a foundation for autonomous marine operations.
66CPG Click Petróleo e Gás2026-04-27
Japan commercially launched the world's first Level 4 autonomous container ship, a 134-meter vessel operating on regular routes. The ship navigates without human intervention, marking a milestone in maritime autonomy. This development could transform coastal logistics by enabling fully autonomous cargo transport. The launch occurred on April 27, 2026.
67Ship & Bunker2026-04-03
ClassNK has granted approval for an autonomous navigation system designed for a container ship. The approval marks a step forward in the integration of autonomous technology in maritime operations. The system's specifics and the vessel involved were not detailed in the provided text.
This paper introduces a **Domain Predicted Area of Danger (DPAD)** model for intelligent anti-collision decision-making in multi-vessel scenarios. The model constructs a **Predicted Collision Risk Area (PCRA)** based on COLREGs guidelines and uses **Potential Collision Risk (PCR)** thresholds to evaluate safe navigation resumption. **Grey Wolf Optimization (GWO)** and **Bézier curves** optimize post-collision recovery paths. Validated with real collision cases, the model demonstrates reliable adaptive control for vessel avoidance in complex waters.
This paper introduces a control system for autonomous surface vessels that handles both trajectory tracking and collision avoidance in dynamic environments. It uses Time to Closest Point of Approach (TCPA), Distance at Closest Point of Approach (DCPA), and Collision Risk Index (CRI) to assess risks and adjust the vessel’s path in real time. An Adaptive Sliding Mode Control (ASMC) ensures precise tracking and resilience against disturbances without needing prior knowledge of their limits. Simulations confirm the method’s effectiveness in avoiding collisions while maintaining stability and accuracy.
This study introduces an MBSE-based method to define, quantify, and verify operational design domains (ODDs) for autonomous ship functions. It converts regulatory, mission, and risk requirements into measurable ODD conditions, ensuring traceability across system architecture, verification scenarios, and runtime logic. The approach is demonstrated through a collision-avoidance case study, where ODD conditions are formalized and tested via model checking, simulations, and risk analysis. Results confirm consistency in model-level verification but do not validate real-world operational safety or threshold effectiveness.
This study developed a solar-powered autonomous boat navigation system for future swarm unmanned surface vehicle (USV) use. The system uses GPS and a digital compass for positioning, controlled by a TTGO ESP32 LoRa microcontroller, transmitting real-time data via LoRa. Testing showed 92% directional accuracy with an average error of 5.4 degrees, demonstrating stable and adaptive navigation. The design provides a scalable foundation for swarm-based environmental monitoring.
72Black Enterprise2026-08-04
Autonomous ships are transforming maritime safety by reducing human error, a leading cause of accidents at sea. These vessels use advanced sensors, AI, and real-time data to navigate and avoid collisions. The technology aims to improve efficiency and reliability in shipping operations. Industry experts predict widespread adoption in the coming years.
73Smart Maritime Network2026-07-17
A Korean-developed robot successfully completed an autonomous shipboard operations test aboard an HMM (Hyundai Merchant Marine) vessel. The trial demonstrated the robot's ability to perform tasks without human intervention in a maritime environment. This marks a step forward in integrating robotic systems for automated operations on commercial ships. The test was reported by the Smart Maritime Network.
VesselNav-Bench is a 2D simulator and benchmark designed for autonomous vessel navigation. It allows direct comparison between classical and learning-based navigation methods using identical physics, scenarios, and scoring. The benchmark provides transparency for each decision made by both approaches. The system is frozen and seeded to ensure consistent evaluation.
75The Korea Post2026-05-29
A U.S. uncrewed vessel completed the first autonomous transit of the Taiwan Strait in May 2026. This event highlights the growing competition in maritime surveillance technology. The development signals increased focus on unmanned systems for naval operations. The move occurs amid rising tensions in the region.
76아시아경제2026-10-28
South Korea aims to lead global orders for fully unmanned autonomous vessels by 2035. The initiative focuses on advancing technology to develop and commercialize these vessels. Government and industry collaboration is emphasized to achieve this goal. The plan targets significant growth in the autonomous shipping market.
77portnews.ru2026-10-22
South Korea’s South Gyeongsang Province has requested approval to designate waters off Geoje Island as an autonomous-ship operating zone, enabling Samsung Heavy Industries to continue sea trials after its current regulatory exemption ends in 2026. The proposed area covers coastal waters near Hansan, Gajo, and Chilcheon islands, with a decision expected by year-end. Samsung Heavy is testing a 12-meter autonomous vessel featuring AI-based navigation, collision avoidance, and sensor systems. The province aims to support the technology while minimizing disruptions to fishing, shipping, and leisure activities.
This paper reviews ship path planning (SPP) advancements, highlighting its role in improving navigation safety and energy efficiency for autonomous ships. It categorizes modeling frameworks, algorithms, and evaluation metrics while addressing challenges like COLREGs compliance, path smoothing, and uncertainty. Hybrid algorithms combining global optimization with real-time local adjustments are identified as the leading solution. Future progress depends on deeper integration of planning with perception, prediction, and control systems.
79Koreabizwire2026-10-19
HD Hyundai and Anduril conducted tests of autonomous ship technology in U.S. waters. The collaboration aims to advance unmanned maritime systems. The trials focused on evaluating the performance and capabilities of the technology. No specific outcomes or details of the tests were disclosed.
80Breakingthenews.net2026-10-18
HD Hyundai and Anduril conducted tests of autonomous ship technology in the U.S. The collaboration aims to advance unmanned maritime systems. The trials focus on evaluating the performance and capabilities of the technology. No specific outcomes or details of the tests were disclosed.
This study explores using Transformer and Performer models to predict ship trajectories in confined waters using AIS data. The approach incorporates positional heat maps, generated via Kernel Density Estimation and processed with a CNN, to capture historical vessel movement patterns. Models trained with quantile loss provide prediction intervals, improving collision avoidance capabilities. Results show significant performance gains over prior methods, particularly in predicting movements not evident from past positions alone.
82Dealroom2026-09-17
Eight Knot secured approximately $2.9 million in funding to expand its autonomous vessel technology internationally. The investment aims to support scaling operations and advancing the company’s maritime automation solutions. The funding round was reported by Dealroom. No additional details on investors or specific expansion plans were provided.
The paper introduces a modified particle filter for maritime target tracking using autonomous surface vessels (USVs) in scenarios with intermittent communication. The method adjusts particle distribution to prevent overconfidence in local estimates when external data is unavailable. Tests with USVs on a lake show the approach maintains performance under normal conditions while improving results in certain edge cases. The work focuses on distributed consensus without centralized coordination.
This paper introduces a LiDAR-based deep reinforcement learning (DRL) framework for autonomous surface vessels (ASVs) to follow paths and avoid static obstacles in narrow waterways. Using a Soft Actor-Critic (SAC) policy trained in simulation, the system processes LiDAR data and motion states to control rudder and propulsion, achieving a 95% success rate in 500 simulated trials with low collision rates. Field tests with a model-scale vessel demonstrated obstacle avoidance but showed increased path oscillations and cross-track errors compared to simulation. The approach relies on compact LiDAR sector features and continuous control commands for real-time navigation.
The paper explores the role of artificial intelligence (AI) in advancing maritime autonomous surface ships (MASSs), highlighting their growing significance in the maritime sector. Authors Chenguang Liu, Jialun Liu, Xiumin Chu, and Xiao Lang discuss AI applications in intelligent ship navigation. The research focuses on how AI technologies enhance autonomy, safety, and efficiency in vessel operations. Published in 2026, the work reflects emerging trends in autonomous maritime systems.
86Chosunbiz2026-06-08
HJ Shipbuilding and HD Hyundai Avikus signed an agreement on June 8, 2026, to collaborate on deploying autonomous ship technology. The partnership aims to integrate Avikus’ autonomous navigation systems into ships built by HJ Shipbuilding. This deal marks a step toward advancing smart shipping solutions in the maritime industry. No specific technical details or timelines were provided in the announcement.
87Lloyd's Register2026-06-02
SK Shipping and HD Hyundai are collaborating with Lloyd’s Register (LR) to develop next-generation autonomous ship designs. The partnership aims to advance maritime autonomy through joint research and innovation. The initiative focuses on creating safer, more efficient autonomous shipping solutions. No specific technical details or outcomes were disclosed in the announcement.
88Smart Maritime Network2026-05-12
A NYK vessel completed an autonomous navigation trial in Singapore in May 2026. The trial tested advanced navigation systems for unmanned ship operations. The project aims to enhance maritime safety and efficiency. Details on specific technologies or outcomes were not provided.
This paper introduces a navigation framework for autonomous ships that improves path-following in curved routes and under wind or current disturbances. It uses a rough-set classifier to detect disturbances and adjusts PID, LQR, and MPC controllers in real time. A Bézier-based method refines paths to match ship maneuvering limits, while Nomoto and MMG models ensure dynamic feasibility. Simulations demonstrate better precision, robustness, and comfort compared to traditional heading-keeping autopilots.
The paper introduces a diffusion route planner (DRP) that models human ship navigation using 181 days of AIS–radar-integrated data from a training ship. In tests, the DRP followed COLREGs Rules 13–17 in one-on-one scenarios and maintained safety margins in complex, multi-ship real-sea conditions. Unlike other data-driven methods, it handled realistic constraints better due to offline training on fused sensor data. The approach also showed potential to incorporate additional rules and local regulations without explicit programming.
This paper introduces an autonomous berthing control system for large container ships, addressing nonlinear hydrodynamics and maneuvering challenges in restricted waters. It uses a four-quadrant propeller model within an improved MMG framework and a dynamic line-of-sight guidance system paired with PD/PI controllers. Validation against S-175 ship data showed errors under 15%, while simulations for the B-573 ship at Yangshan Port demonstrated a 62% reduction in trajectory error under strong currents. The system achieved smooth deceleration from 6.4 m/s to a full stop in 588 seconds, confirming its feasibility in complex maritime conditions.
92vocal.media2026-04-08
The autonomous ships market is projected to grow significantly by 2033, driven by advancements in collision avoidance and AI control systems. Key trends include increased adoption of automation to improve maritime safety and efficiency. The report highlights industry forecasts, emphasizing the role of AI in enhancing navigation and operational decision-making. Market expansion is expected across commercial and defense sectors.
93부산일보2026-11-05
The 20th World Maritime Forum highlighted the need for Small Modular Reactor (SMR) ships, AI-driven autonomous vessels, and a transition to green energy in the maritime industry. Experts emphasized these technologies as essential for future shipping efficiency and sustainability. The forum discussed their commercialization and integration into global maritime operations. No specific implementation timelines or technical details were provided.
94뉴스핌2026-10-28
South Korea’s Ministry of Oceans and Fisheries aims to lead globally in autonomous ship technology. It plans to integrate AI in ports to boost productivity by 30%. The initiative seeks to enhance efficiency and competitiveness in maritime operations. No specific methods or outcomes beyond the productivity target are detailed.
95제이앤엠뉴스2026-10-28
The Ministry of Oceans and Fisheries plans to achieve global leadership in fully autonomous ships by 2035. It is driving an AI transformation across all marine and fisheries sectors. The initiative aims to modernize and enhance efficiency in these industries. No specific methods or interim results are detailed in the announcement.
96영남신문2026-10-22
A 2026 event showcased future maritime technologies, including AI-powered autonomous ships and underwater robots. The exhibition took place inland, highlighting advancements in unmanned and robotic systems for marine applications. These innovations aim to improve efficiency and safety in maritime operations. The event was reported by *Yeongnam Ilbo*.
97세종경제뉴스2026-10-21
A 2026 event showcased future maritime technologies, including AI-powered autonomous ships and underwater robots. The exhibition took place inland, highlighting advancements in unmanned and robotic systems for marine applications. These innovations aim to improve efficiency and safety in maritime operations. The event demonstrated emerging trends in the industry.
98헤럴드경제2026-10-21
The Korea Ship and Ocean Engineering Research Institute presented advanced maritime technologies, including AI-powered autonomous vessels and underwater robots. The showcase highlighted future developments in ocean engineering. No specific technical details or outcomes were disclosed. The announcement was reported by 헤럴드경제 on October 21, 2026.
This paper introduces a hierarchical robust control system for autonomous berthing of Maritime Autonomous Surface Ships (MASS) to handle dynamic propulsion-mode switching and environmental disturbances. The method includes a reverse spatial-temporal planning algorithm, a decoupled adaptive guidance law, and a dynamic-truncation SQP allocation strategy to improve thrust management and actuator transitions. Testing under strong wind-current conditions (Beaufort force 7) demonstrated sub-meter berthing accuracy and reduced yaw-rate deviation by 13.8% compared to conventional methods. The system also prevented collisions with the shoreline during sudden disturbances.
This paper introduces an improved hp-pseudospectral (IHP) method for optimizing autonomous ship berthing paths in complex ocean currents. Unlike traditional methods, it avoids assuming constant surge velocity or thrust, enabling simultaneous optimization of speed and energy consumption. The IHP method divides time intervals, uses subdomain collocation, and transforms optimization to achieve Pareto-optimal paths. Simulations show it outperforms A* and genetic algorithms, reducing travel distance, sailing time, and energy consumption by up to 6.9%.
The paper provides a reproducibility package for the CAεL-A* algorithm, a two-stage search method optimizing travel time and maneuvering for autonomous surface vessels. It includes source code, 540 synthetic current fields, Strait of Gibraltar data (from NOAA/NCEP and Natural Earth), and raw results from 11,880+ runs. The package also contains derived tables, figures, and scripts to regenerate analyses without rerunning searches. Two archive versions are offered: a full dataset with checkpoints and a smaller, aggregated version.
102The Globe and Mail2026-07-21
Davie, a Canadian shipbuilding company, is entering the autonomous vessel industry. The expansion marks a shift toward advanced maritime technology. No specific projects or partnerships were detailed in the announcement. The move aims to position Davie as a leader in uncrewed shipping solutions.
This paper examines robust formation control for networked autonomous surface vessels (NASVs) considering input dead-zone and decision-making constraints. It introduces a noncooperative game framework to enable flexible formation and proposes two game-based approaches: one with fast compensation for input dead-zones and another using a penalty function for decision-making constraints. The methods rely on distributed formation with partial information and are validated through simulations. Convergence analysis employs a two-time-scale method.
104Travel And Tour World2026-05-04
Orca AI and Samsung Heavy Industries have partnered to develop autonomous vessel technologies for the cruise tourism industry. The collaboration aims to enhance navigation and safety features using AI-assisted systems. This initiative is expected to support the growth of cruise tourism by improving operational efficiency and reliability. The project focuses on integrating advanced AI solutions into maritime operations.
This study presents a nonlinear model predictive control (NMPC) method for autonomous ship berthing, using a 4-DOF (sway, surge, yaw, roll) dynamic model. The approach integrates moving horizon estimation (MHE) and real-time trajectory optimization to adapt to environmental changes. Simulations in the Port of Hamburg showed track errors under 2 meters and berthing position errors of just 0.3 meters. The method outperforms traditional PID and NMPC controls in rough sea conditions, improving reliability for unmanned ships.
106Smart Maritime Network2026-11-06
Jiangsu Ocean Shipping is testing autonomous technology on two 120 TEU electric container ships, *Jiangyuan Jinling* and *Jiangyuan Qinhuai*, along an 80 km stretch of the North Jiangsu Canal. The vessels will use radar, AIS, and high-definition sensors to navigate varying conditions, including bends and bridges, with tailored navigation settings for each environment. The project aims for 90% object recognition accuracy and expects autonomous navigation to cover at least one-third of the trial distance. A digital twin and shore-based interface will assist with voyage planning, risk alerts, and port coordination.
This study introduces a reliability-based robust design optimization (RBRDO) method for improving unmanned surface vessel berthing trajectory planning under uncertainty. The approach integrates line-of-sight guidance, artificial-potential-field obstacle avoidance, and sliding-mode control, while modeling collision risks for various obstacle shapes and accounting for vessel-position uncertainty. Simulations demonstrate that RBRDO outperforms baseline and deterministic optimization methods by reducing terminal distance errors, failure probability, and variability in berthing performance. The method enhances robustness and reliability in uncertain berthing conditions.
108Ocean News & Technology2026-09-28
Omnes Capital has invested in Maritime Robotics to expand the development and deployment of autonomous maritime systems. The funding aims to scale the company’s technology for unmanned surface vessels. Maritime Robotics specializes in advanced navigation and operational solutions for the maritime industry. The partnership focuses on enhancing efficiency and safety in marine operations.
109Splash2472026-09-23
German researchers are creating an AI system to assist vessel traffic controllers in managing waterways with conventional, automated, and autonomous ships. The LEAS-PRO project, led by Fraunhofer FKIE, will develop a demonstrator that analyzes traffic data and provides risk assessments and recommendations to operators. The goal is to enhance situational awareness and reduce cognitive strain without replacing human controllers. The project is set to continue until July 2028.
110Smart Maritime Network2026-09-16
HD Hyundai Samho, Avikus, and ClassNK signed an agreement to collaborate on designing autonomous navigation systems for ships. The partnership aims to develop and certify advanced technologies for unmanned vessel operations. The project will focus on integrating AI-driven navigation solutions into commercial shipping. ClassNK will provide regulatory and safety certification for the designs.
The paper introduces QSD-STFormer, a quaternion ship domain-guided spatiotemporal transformer for ship trajectory prediction in dense maritime environments. It combines a CVAE framework with a QSD-guided Interaction Prior Module to incorporate physical navigation constraints, improving data-driven modeling. A Spatiotemporal Joint Modeling module captures dynamic ship interactions, while a GMM-Latent Representation module enhances prediction diversity. Tests on AIS data from the Port of New York and Wuhan Riverfront show a 15.28% reduction in minADE and 24.15% in minFDE compared to existing methods.
112crossref2026-09-08Sergey I. Kondratyev, Evgeniy V. Khekert, Nikita V. Martyushev, Boris V. Malozyomov +5 moreDOI ↗ (opens in a new tab) A risk-aware multisensor fusion system was developed for autonomous navigation and mooring in confined waters, integrating data from RTK-GNSS, inertial sensors, radar, LiDAR, cameras, and other sources. The system uses fault detection, sensor-quality assessment, and dynamic authority allocation between automation and operators to maintain reliability. Testing over five days with 40 runs and 480 evaluation epochs showed horizontal-position and heading errors of 0.043 m and 0.176°, respectively, under normal conditions. The system detected all 64 fault events, with 99.3% protection-level coverage for single faults and 100% for combined faults, ensuring safe operation even under degraded sensor conditions.
113Baird Maritime2026-08-13
Greek companies have formed a partnership to develop autonomous vessels. The collaboration aims to advance maritime technology in Greece. No specific companies or technical details were provided in the announcement. The initiative reflects growing interest in unmanned shipping solutions.
This study introduces a Deep Q-Network (DQN)-based path planning method for autonomous fishing vessels to enhance safety by reducing wave-induced roll motions. The approach integrates roll motion reduction into the reward function and combines maneuvering models with hydrodynamic analyses. Simulations showed the vessel adjusted its heading to minimize roll, reducing average and maximum roll responses by 3% and 2%, respectively, at a 1.0 m wave height, while maintaining path tracking with a slightly longer route. Future work will include testing in more complex environments and validating the model through physical experiments.
115Baird Maritime2026-07-24
Saronic and Samsung Heavy Industries have formed a partnership to develop autonomous shipbuilding technology. The collaboration aims to advance innovations in uncrewed vessel construction and operations. This initiative focuses on integrating automation into ship design and manufacturing processes. The details of specific technologies or timelines were not provided.
This paper introduces a reinforcement learning method called SAC-LSTM for autonomous ships to track moving targets. It uses LSTM networks to analyze past motion patterns and improves tracking by balancing distance, heading, speed, and smooth control. Tests show SAC-LSTM outperforms other methods, increasing success rates by 7.5% and reducing heading errors by 35.5%. The approach proves effective under various motion conditions and disturbances.
117Digital Ship2026-05-21
The MODULARIS project by DLR provides shipowners with a live testing environment for digital and autonomous vessel systems. It serves as a practical platform to evaluate and develop new maritime technologies. The initiative aims to advance innovation in ship automation and digitalization. No specific results or methods beyond the testbed concept are detailed.
This study introduces a hybrid deep learning model combining CNNs, Transformers, and MLPs to improve ship trajectory prediction using AIS data. The CNN extracts local motion patterns, while the Transformer captures long-range temporal dependencies, and the MLP decodes features for accurate forecasts. K-means clustering enhances model generalization by adding static navigational features. The model outperformed baseline methods, achieving a root mean square error of 1.56 km and a mean absolute error of 1.07 km at a 60-minute prediction horizon.
119Naval Today2026-11-05NavalToday
EDGE signed two MoUs in November 2026 to advance unmanned surface vessel (USV) development and engine integration. The first agreement involves EDGE Europe and Novamarine, combining Italian-built platforms with an autonomous control system from EDGE’s Technology Innovation Institute (TII). The second MoU focuses on integrating CMD’s Italian-designed engines into vessels built by Abu Dhabi Ship Building (ADSB). These partnerships aim to strengthen cooperation between EDGE’s European and UAE-based entities in naval shipbuilding and autonomous maritime technologies.
120Splash2472026-10-28
South Korea plans to develop fully autonomous ships by 2032 as part of a broad AI strategy. The initiative aims to integrate artificial intelligence into maritime operations. The government is driving this effort to enhance efficiency and competitiveness in the shipping industry. No specific technical details or implementation steps were provided in the announcement.
This study presents a multi-fidelity reinforcement learning (RL) framework to improve the development of autonomous surface vehicles (ASVs). The approach trains policies first in a low-fidelity simulation for speed, then refines them in high-fidelity physics, reducing training time by 77.1% and enabling testing of 80 policy configurations within the same computational budget. The method successfully transferred navigation skills—waypoint reaching, station-keeping, and obstacle avoidance—to a real ASV in a harbor under Sea State 4 conditions. Results confirm the framework effectively bridges the simulation-to-reality gap while enhancing safety and cost-efficiency.
This study presents a hierarchical path-planning framework for unmanned surface vehicles, combining global routing and local collision avoidance while adhering to maritime rules. The method integrates adaptive A* and RRT* algorithms for global planning, alongside artificial potential fields and dynamic window approaches for local adjustments. Simulations showed 81.7% success in static planning, with lower success rates (63.3%) in integrated navigation under perturbed conditions. The approach proved feasible but highlighted inefficiencies like detours and stalled trajectories, requiring further improvements.
This paper updates maritime security research, addressing challenges from digitization, automation, cyber-physical risks, and geopolitical threats. It expands on risk identification, decision-making, and system resilience, covering collision-risk sensing, human-technology interaction, and port safety. The studies highlight gaps like limited validation, incomplete causal analysis, and weak cross-scale integration. Effective implementation demands trustworthy data, uncertainty management, and coordinated response plans for cyber-physical and security failures.
124safety4sea2026-09-29
HD Hyundai is testing physical AI to enable autonomous operations in shipyards. The technology aims to improve efficiency and safety in shipbuilding processes. The trial focuses on integrating AI with physical systems for real-time decision-making. No specific outcomes or results have been reported yet.
SeaTrack-DeepSORT is a vision-based framework for ship detection and multi-object tracking in complex maritime environments, addressing challenges like scale variations, occlusions, and camera motion. It combines SeaDet-YOLO, an enhanced YOLOv11 detector with ADown downsampling and a P2 detection head, with a modified DeepSORT tracker that uses confidence-dependent updates and motion-compensated association. The system improves detection and tracking accuracy while ensuring stable trajectories. Experiments on maritime datasets demonstrate its effectiveness for autonomous perception in unmanned surface vehicles.
This study explores factors influencing Indian tourists' acceptance of Autonomous Maritime Tourism (AMT), an AI-driven innovation in smart ports and navigation. Using the Technology Acceptance Model and Diffusion of Innovation theory, it surveyed 237 Indian tourists and analyzed data via discriminant function analysis. Social influence emerged as the strongest predictor of behavioral intention, followed by relative advantage, perceived complexity, and facilitating conditions. The findings highlight that social validation and perceived benefits outweigh risk factors in shaping AMT adoption in India.
127Splash2472026-09-21
Cido Shipping has selected autonomous navigation systems for 20 of its Pure Car and Truck Carriers (PCTCs). The decision aims to enhance operational efficiency and safety in maritime transport. No specific technology provider or timeline for implementation was disclosed in the announcement. The move reflects a growing industry trend toward automation in shipping.
128EdexLive2026-09-18
A team of students from IIT Madras won the Njord Autonomous Ship Challenge held in Norway. The competition focused on designing and operating autonomous ships. The event took place in September 2026. Their victory highlights the team's expertise in maritime automation and engineering.
129ETV Bharat2026-09-18
IIT Madras' Team Aritra secured first place in the Njord Autonomous Ship Challenge held in Norway. The competition focused on developing autonomous navigation and operational capabilities for unmanned vessels. The team's performance highlighted advancements in maritime robotics and AI-driven systems. The event took place in September 2026.
130India Today2026-09-18
IIT Madras ocean engineering students secured first place in an international autonomous ship-building competition. The event focused on designing and constructing unmanned, self-navigating vessels. The team's innovation and technical skills were recognized as the best among global participants. The win highlights India's growing expertise in maritime automation technology.
131Education Times2026-09-18
A team of Ocean Engineering students from IIT Madras won the Njord Autonomous Ship Challenge held in Norway. The competition focused on designing and operating autonomous ships. The victory highlights the students' expertise in maritime technology and innovation. The event took place in September 2026.
The paper discusses the MONUSEN project, an EU-funded initiative enhancing public engagement in marine robotics to support UN Sustainable Development Goal 14 (Life below Water). It involved two public activities: demonstrating MARUS, a simulator for realistic maritime environments, and allowing participants to pilot the SWAMP autonomous surface vehicle (ASV) for data collection. These activities successfully engaged diverse groups, improving public understanding of autonomous vehicle control and marine technology. The project bridged gaps between citizens and scientists, fostering interdisciplinary collaboration.
133Puente de Mando2026-06-18
The electric and autonomous ship *Yara Birkeland* completed its first operational voyage in June 2026. The vessel is designed for zero-emission cargo transport, operating without a crew. This milestone marks a step toward sustainable and automated maritime shipping. The project aims to reduce emissions and improve efficiency in short-sea shipping.
This study analyzes ship safety domains in the North Sea using AIS data from 2014, focusing on vessels over 45 meters long traveling at 5–25 knots. Results confirm that safety domains are elliptical, with the major axis expanding at higher speeds while the minor axis remains constant, and ship size does not significantly affect domain dimensions. Avoidance maneuvers rely on CPA and TCPA calculations, typically initiated 600–1800 seconds before the closest point of approach, with modest course changes to maintain the elliptical domain. The findings suggest elliptical domains could improve conflict-detection algorithms and navigation systems for both crewed and autonomous ships.
This paper introduces an Autonomous Guided Vehicle (AGV) that uses Vision-Language Models to navigate indoor spaces based on natural language commands. The system, built on a Raspberry Pi 5 with a camera, ultrasonic sensors, and motors, interprets spoken instructions and combines visual and language data to identify targets and avoid obstacles. Tests in various indoor settings showed high accuracy in command recognition and reliable navigation performance. The approach improves flexibility over traditional fixed-route AGVs in dynamic environments.
This paper introduces ODDIT, a digital twin-based method for detecting out-of-distribution (OOD) states in autonomous vessels (AVs) before they occur. The approach uses two machine-learning models to predict future vessel states and identify potential OOD conditions, enabling proactive interventions. Evaluated on five vessels under simulated conditions with noise and environmental disturbances, ODDIT achieved 99% accuracy in OOD detection, measured by AUROC and TNR@TPR95 scores. The method supports predictive maintenance, fault diagnosis, and scenario-based testing for AVs.
137Safety4Sea2026-06-03safety4sea
The article reports on newly launched initiatives aimed at improving AI infrastructure and the design of autonomous vessels. These efforts focus on enhancing technological capabilities in maritime operations. The projects seek to support innovation in shipping and related industries. No specific methods or outcomes are detailed in the text.
138Marine Insight2026-05-25
Germany has launched MODULARIS, a new floating laboratory designed to test autonomous ship technologies and advanced propulsion systems. The facility will serve as a research platform for future maritime innovations. The project aims to accelerate the development of smart and sustainable shipping solutions. No specific testing results or methods have been disclosed yet.
The paper *Sim2Sea* introduces a framework to improve autonomous maritime navigation in congested waters by addressing the gap between simulation and real-world performance. It features a GPU-accelerated simulator, a dual-stream spatiotemporal policy with safety mechanisms, and domain randomization to enhance real-world transfer. Tests show the method achieves faster training and safer navigation than existing approaches. The policy, trained only in simulation, successfully operated a 17-ton unmanned vessel in real congested waters without additional adjustments.
140日本郵船株式会社2026-05-22
The MEGURI2040 program, led by 日本郵船株式会社 (NYK Line), aims to develop fully autonomous ships using advanced technologies to address social challenges. The initiative focuses on creating world-leading innovations in maritime autonomy. The program is part of efforts to enhance safety, efficiency, and sustainability in shipping. No specific results or methods beyond the program's goals are detailed in the text.
141CPG Click Petróleo e Gás2026-05-22
The Chinese autonomous ship V180 is 24 meters long and operates without a crew in open waters. It can independently launch and recover ROVs, with a depth capability of up to 3,000 meters. The vessel can perform missions for up to 30 days without human intervention.
142Digital Ship2026-05-13
ZeroUSV and Manor Marine are expanding production of autonomous vessels as UK operators gear up for large-scale deployment. The collaboration aims to meet growing demand for uncrewed maritime solutions. This effort reflects broader industry shifts toward automation in shipping. The focus is on scaling operations to support future commercial and operational needs.
143조선일보2026-05-11
The article reports on an AI Autonomous Navigation Competition that could shape the future of shipbuilding. Held in 2026, the event highlights advancements in autonomous shipping technology. It suggests that the outcomes may influence industry standards and innovation. The competition aims to determine the most effective AI-driven navigation systems for maritime use.
144NavalToday.com2026-05-08
Blue Water Autonomy is expanding its autonomous ship program by adopting a new manufacturing model. The company aims to scale up production of unmanned vessels. Details on the specific manufacturing changes or timeline were not provided. The initiative focuses on advancing autonomous maritime technology.
This paper introduces a structured method for designing autonomous ships by combining traditional ship design principles with autonomy-specific requirements. The approach helps generate early concept designs optimized for operational needs, assessing how autonomy affects ship dimensions, layouts, and systems. A case study on a feeder containership demonstrates the method’s application. Future research aims to expand the focus beyond the ship itself to entire autonomous maritime ecosystems.
146Manifold Times2026-04-30
The Maritime and Port Authority of Singapore (MPA), Nippon Yusen Kabushiki Kaisha (NYK), and Monohakobi Technology Institute (MTI) conducted autonomous ship trials in Singapore. The trials aimed to test and advance autonomous navigation technologies for maritime vessels. This collaboration involved practical demonstrations of ship autonomy in real-world conditions. The initiative reflects efforts to enhance safety and efficiency in maritime operations.
147Digital Ship2026-04-30
NYK successfully demonstrated the real-world integration of autonomous vessel systems in Singapore. The trial showcased the practical application of these systems in a busy maritime environment. The initiative highlights advancements in autonomous shipping technology. The project aims to improve safety and efficiency in maritime operations.
148Yahoo Finance2026-04-14
Fairbanks Morse Defense’s Vestdavit introduced the first industry Autohook system designed for fully autonomous vessel recovery. The technology aims to enable unmanned vessel operations by automating the recovery interface. This innovation targets improved efficiency and safety in maritime operations. The system was unveiled on April 14, 2026.
149Chosunbiz2026-04-07
Avikus received approval from DNV, a leading classification society, to accelerate the global deployment of its autonomous ship technology. This certification supports the company’s efforts to expand its self-navigating systems in the maritime industry. The approval is expected to facilitate faster adoption and regulatory acceptance of autonomous vessels worldwide. The development marks a significant step in advancing automated shipping solutions.
150搜狐网2026-11-04
The Ouka Smart Ship received China's first type approval certificate from the China Classification Society (CCS) for its "Unmanned Boat Perception and Autonomous Navigation System." This certification marks a milestone in China's maritime industry for unmanned vessel technology. The system enables autonomous navigation and environmental perception for unmanned boats. The achievement highlights progress in smart shipping development.
151헤럴드경제2026-10-28
South Korea aims to become the world leader in autonomous ships by 2035, with crewless vessels navigating the seas and AI managing container operations. The country is advancing technology to enable unmanned maritime transport. Challenges remain in achieving full autonomy and global regulatory alignment. The initiative reflects efforts to modernize shipping and enhance competitiveness.
152파이낸셜뉴스2026-10-28
The global autonomous ship market is targeting Level 4 autonomy within six years. This advancement aims to secure fully automated navigation technology for vessels. The focus is on developing systems that require minimal to no human intervention. Industry efforts are accelerating to achieve this milestone by 2032.
153매일신문2026-10-28
A South Korean initiative aims to develop fully unmanned autonomous vessels by 2032. The project will integrate AI into ports and fishing grounds to enhance operations. The focus includes improving navigation, safety, and efficiency in maritime activities. The plan reflects broader efforts to modernize the shipping and fishing industries.
154Smart Maritime Network2026-10-28
South Korea plans to achieve fully autonomous shipping by 2032. The initiative aims to integrate advanced technologies into maritime operations. The government and industry stakeholders are collaborating on this long-term project. The goal includes enhancing safety, efficiency, and competitiveness in the shipping sector.
155뉴시스2026-10-28
South Korea’s Ministry of Oceans and Fisheries plans to develop fully unmanned autonomous ships by 2032. The initiative aims to advance maritime technology and improve shipping efficiency. No specific technical details or intermediate milestones were provided in the announcement. The project reflects broader efforts to modernize the country’s maritime industry.
156PortNews IAA2026-10-28
South Korea will invest $346 million by 2032 to develop fully autonomous (Level 4) ships, aiming to lead global orders for the technology by 2035. The project, led by two ministries, includes unmanned navigation, automated systems, and testing in domestic and international waters. It builds on a prior $120 million program (2020–2025) that achieved Level 3 autonomy. The initiative is part of a broader maritime AI strategy to boost productivity, safety, and environmental management.
157enterpriseam2026-10-25
Saudi Arabia aims to launch self-navigating ships in its waters by the end of 2027, following trials since 2025, while also developing local ship-component manufacturing. The International Maritime Organization’s voluntary autonomous shipping code took effect in 2026, but mandatory rules won’t apply until 2032. Bahri signed a $453 million contract with IMI to build three crude carriers, with deliveries between 2029 and 2030, as part of a broader 10-year vessel construction agreement. Almarai plans to expand into red meat and seafood production, investing SAR 4 billion to grow revenue from SAR 24 billion to SAR 36 billion over five years.
158해양레저신문2026-10-22
A 2026 event introduced youth to maritime technology research, focusing on autonomous ships and underwater robots. Organized by 해양레저신문, it aimed to showcase advancements in the field. The initiative highlighted real-world applications of maritime innovations. No specific outcomes or participant numbers were detailed.
159gCaptain2026-10-21
The U.S. Navy is advancing unmanned surface vessels (USVs) from testing to operational use, with recent deployments in the Persian Gulf and alongside the USS Theodore Roosevelt Carrier Strike Group. Successful missions include rescuing downed Army aviators and supporting mine clearance, demonstrating USVs' role in real-world operations. Senior leaders, including the Secretary of War and Navy officials, are prioritizing integration into fleet operations, emphasizing organizational and training reforms. The Navy must now develop clear concepts of operations (CONOPS) to define missions, ownership, and sustainment while continuing experimentation without treating it as a standalone strategy.
160미디어인뉴스2026-10-20
South Gyeongsang Province plans to designate zones for autonomous ship operations, replacing human navigators with AI. The initiative aims to advance maritime technology by allowing AI-controlled vessels to navigate these areas. No specific timeline or technical details were provided in the announcement. The province is leading efforts to modernize shipping through automation.
161해양레저신문2026-10-19
South Gyeongsang Province is working to designate demonstration zones for autonomous vessels to support Samsung Heavy Industries' technology development. The initiative aims to create designated areas where self-navigating ships can be tested and validated. This effort is part of broader regional support for advancing maritime automation. The province seeks to strengthen collaboration with industry leaders in autonomous shipping.
This paper reviews technologies enabling agricultural robots to autonomously follow crop rows for tasks like weeding and harvesting. It examines perception methods (vision, LiDAR, and multimodal fusion) and their effectiveness under challenging conditions like lighting changes and weed interference. The review also covers navigation techniques, state estimation, path planning, and motion control, comparing their accuracy, real-time performance, and adaptability. It provides a framework for integrating perception, planning, and long-term operation in complex agricultural settings.
This study introduces Extended Higher Order Dynamic Mode Decomposition incorporating Control Input (EHODMD-CI) to improve ship maneuvering motion prediction for autonomous vessels. The method enhances prior HODMD-CI by adding an Abkowitz-inspired nonlinear dictionary to better capture motion nonlinearity and rudder angle coupling. Tested on numerical simulations and real-world ship trial data, EHODMD-CI outperformed HODMD-CI, LSTM, Gaussian Process Regression, and Transformer in accuracy (lowest RMSE, highest Pearson Correlation Coefficient) and computational efficiency. The approach offers faster training and prediction times compared to deep learning and probabilistic methods.
164녹색경제신문2026-09-28
Maeum AI is expanding into fully autonomous ships, accelerating its transition into physical AI. The company aims to integrate AI-driven systems for unmanned vessel operation. This move marks a significant step in advancing AI applications beyond digital spaces. The initiative is expected to drive broader adoption of physical AI technologies.
This study highlights the need for rich datasets to accurately model ship maneuvering hydrodynamics for autonomous surface vessels (ASVs). Current methods using standard maneuvers like turning circles or zig-zag tests fail to capture nonlinear interactions and higher-order effects. The authors propose a structured approach to measure data richness and introduce a new maneuver to improve the estimation of hydrodynamic coefficients. The findings aim to enhance the reliability of ASV guidance and control systems in complex scenarios.
166India Shipping News2026-09-19
HD Hyundai Samho, Avikus, and ClassNK signed a memorandum of understanding (MOU) on September 19, 2026, to collaborate on design verification for an autonomous navigation system. The partnership aims to advance the development and validation of autonomous shipping technologies. The agreement focuses on ensuring compliance with industry standards and regulatory requirements. The initiative reflects efforts to enhance maritime automation and safety.
This paper introduces a Risk-Aware Multimodal Temporal Proximal Policy Optimization algorithm for autonomous collision avoidance in unmanned surface vehicles navigating dynamic maritime environments with varying obstacles. The method uses a collision risk index and Transformer-based network to encode risk maps and vehicle states, improving spatiotemporal feature extraction. Training is enhanced with curriculum learning, progressing from simple to complex tasks. Simulations demonstrate improved performance in success rate, safety, and adaptability compared to baseline methods.
This study introduces a dual-agent hierarchical reinforcement learning framework (HRL-MOO) to improve typhoon-avoidance ship routing under dynamic wind, wave, and current conditions. The model uses a global agent to assess large-scale environmental risks and a local agent to adjust short-term vessel movements, incorporating real-world bathymetric constraints. Simulations of Typhoon Yagi (2024) show the approach reduces environmental risk, improves path smoothness, and speeds up training convergence compared to single-agent methods. Ablation studies confirm the benefits of combining macro-level risk assessment with adaptive multi-objective optimization.
This study identifies five stages of autonomous navigation in intelligent ships, based on IMO and China Classification Society guidelines. It proposes a competency framework for Navigation Technology students, emphasizing skills like smart equipment use, remote control, human-machine collaboration, and data analysis alongside traditional seamanship. The authors recommend updating training objectives, curricula, practical teaching, and assessments to align with these evolving demands. The findings aim to guide educational reforms in navigation programs.
This paper introduces a centralized model predictive control (MPC) framework for autonomous surface vessels that accounts for ship-to-ship interactions, which are often ignored or oversimplified in traditional methods. The approach uses an empirically derived, three-degrees-of-freedom interaction model based on computational fluid dynamics to estimate proximity-induced forces in real time. Simulations show the MPC controller improves trajectory tracking and disturbance handling compared to PID controllers, particularly in platooning, parallel, and triangular formations. The study also examines performance variations across different formation types.
This paper proposes a method to define seakeeping performance criteria for unmanned surface vessels (USVs) to improve their operation in harsh maritime conditions. It reviews seakeeping data from various vessels, including manned ships, to develop a tailored procedure for USVs. A 7-meter-class USV serves as a case study, where preliminary criteria were established and validated through model tests. The goal is to enhance USV design for better performance in joint or independent naval missions.
This study introduces TCN–KAN–PINN, a framework combining a multi-scale Temporal Convolutional Network (TCN), a Linear-KAN predictor, and physics-informed training to improve vessel trajectory prediction. The model extracts temporal features from historical AIS data, corrects nonlinear maneuver deviations, and enforces kinematic consistency using a time-discretized residual and adaptive loss balancing. Tested on real-world AIS data, it outperformed existing methods in accuracy, speed, and course consistency while maintaining efficient inference. Ablation studies confirmed the benefits of kinematic supervision for short-term predictions and the KAN pathway for longer horizons.
This study introduces a framework converting water-segmentation masks into navigation boundaries, centerlines, and corridor metrics for inland autonomous surface vehicles. Tested on 173 low-resolution images, the proposed Edge U-Net outperformed other models, achieving an IoU of 0.964 and a Boundary-F1 score of 0.685. The evaluation highlights discrepancies between pixel-overlap scores and boundary accuracy, using additional indices like the Navigation Robustness Score. The framework focuses on reproducibility and geometric assessment but does not ensure obstacle-aware or closed-loop navigation safety.
The paper introduces an enhanced A* algorithm for path planning in unmanned surface vehicles (USVs) used for inland-river garbage collection, addressing both static and flowing water environments. In static conditions, the method optimizes for shortest path, time, and energy consumption, while in flowing water, it incorporates flow direction into its heuristic and uses a velocity-based energy model. Simulations demonstrate a 3.5% energy reduction with the minimum-energy path compared to the shortest-path approach. The improved A* algorithm outperforms RRT and PRM in optimality, efficiency, and adaptability across various river channels.
175comparethecloud.net2026-10-05
Britain should prioritize autonomous technology investments in harbor operations over ocean crossings, focusing on defined transport tasks like container transfers between terminals. Singapore’s autonomous feeder vessel program serves as a model, emphasizing reliability, safety, and integration with existing workflows rather than immediate cost savings. British suppliers could contribute by addressing specific challenges such as degraded visibility, cybersecurity, or terminal integration, but must clarify responsibilities and boundaries in contracts. Regulatory approval in the UK depends on vessel size, operating area, and use, requiring early engagement with the Maritime and Coastguard Agency to establish a clear pathway.
176Smart Maritime Network2026-09-16
Robosys has integrated its autonomous navigation technology with FarSounder sonar systems. The collaboration aims to enhance maritime situational awareness and obstacle avoidance. This integration allows vessels to operate more safely in complex environments. The solution is designed for both commercial and defense applications.
This study assesses MEMS inertial sensors for autonomous naval platforms, comparing low-cost and tactical-grade sensors under calm and rough sea conditions. Low-cost sensors show rapid error growth without external aiding, while tactical-grade sensors offer better stability. An Extended Kalman Filter (EKF) reduces drift by 60–80% when combined with periodic GNSS updates. Performance declines in high sea states due to increased noise and nonlinear motion.
This paper introduces an adaptive reinforcement learning method for autonomous surface vehicles that allows a single control policy to work across different platforms without prior knowledge of their dynamics. The approach uses a teacher-student architecture to infer platform-specific dynamics from interaction history, enabling zero-shot deployment. Tested on two real-world platforms, the method outperformed non-adaptive baselines by up to 58% in position accuracy while nearing the performance of platform-specific controllers. The policy was trained in simulation with randomized dynamics and required no fine-tuning for real-world use.
This study combines Belief-Rule-Based (BRB) reasoning and the Velocity Obstacle (VO) algorithm to improve ship collision avoidance in dense maritime traffic. BRB reasoning groups ships by movement trends and proximity, simplifying decision-making, while the VO algorithm generates safe avoidance paths. Safety domain radii for ships and groups are adjusted based on ship length. Testing in two scenarios demonstrated enhanced navigational safety and efficiency in congested waters.
This paper examines how AI and autonomous systems in marine environments redistribute responsibility between humans, algorithms, and ecosystems. It introduces an *Ethics of Autonomy* framework, combining posthuman design theory, Actor–Network Theory, and explainable AI to balance human oversight with machine decision-making. Three case studies—FloatyBoats, Seaswarm, and Vatoz—demonstrate how adaptive robotic systems shift from monitoring to active ecological intervention. The authors argue that ethical innovation requires transparent, interpretable, and accountable algorithmic infrastructures.
181Seatrade Maritime News2026-05-12
A NYK car carrier successfully completed an autonomous navigation trial in Singapore in May 2026. The trial tested the vessel's ability to navigate without human intervention. This marks a step toward advancing autonomous shipping technology. The project aims to improve maritime safety and efficiency.
The paper introduces CD-HSSRL, a reinforcement learning framework for autonomous amphibious robots navigating both water and land. It addresses challenges like discontinuous dynamics and safety risks by combining a global reachability planner, hierarchical switching policies, and a safety-constrained controller. Tests on multiple benchmarks show a 15% improvement in transition success and a 40% reduction in collisions compared to existing methods. The framework offers a unified solution for safe cross-domain navigation in amphibious robots.
183The Maritime Executive2026-04-07
Japan and South Korea are progressing in autonomous navigation technology for maritime applications. Both countries are developing systems to enhance ship safety and operational efficiency. The advancements aim to reduce human error and improve navigation in challenging conditions. Collaborative efforts and regulatory frameworks are being established to support these innovations.
184Smart Maritime Network2026-04-07
Avikus received approval from DNV for its autonomous navigation technology. The certification validates the system's compliance with maritime safety and performance standards. This approval marks a step toward broader adoption of autonomous shipping solutions. The technology aims to enhance navigation efficiency and reduce human error in maritime operations.
185YouTube on MSN2026-10-25
The Armada project by Ocean Infinity deploys a fleet of autonomous ships for deep-sea exploration and ocean floor mapping. These advanced vessels use smart technology to navigate and collect data from uncharted depths. The initiative aims to improve understanding of the ocean while reducing human risk in hazardous environments. The project represents a significant step in modern maritime innovation.
186Interesting Engineering2026-10-19
A 368-ton former US Navy vessel, modified with HD Hyundai’s VAone autonomy suite and Anduril’s Lattice platform, completed a 3.5-hour autonomous sea trial off Long Beach, California. The exercise tested navigation, formation sailing, mission adaptability, and responses to simulated machinery failures, monitored remotely by US and South Korean defense officials. The vessel demonstrated integrated autonomy in navigation, machinery management, and damage control without onboard crew intervention. The trial aimed to showcase technology for future unmanned naval operations, though no procurement award has been announced.
187Åbo Akademi2026-10-12
Students from Åbo Akademi tested an autonomous vessel in real-world conditions on Åland after initial simulations. The project aimed to transition the technology from virtual testing to practical maritime application. The trial focused on evaluating the vessel's performance and navigation capabilities in open water. No specific outcomes were detailed in the provided text.
188WorkBoat2026-10-08
Sea Machines has appointed Bradbury to head its autonomous vessel programs. The move aims to strengthen the company’s leadership in maritime autonomy. Bradbury will oversee the development and implementation of self-navigating ship technologies. The hiring was announced on October 8, 2026.
This paper presents a formation control framework for unmanned surface vessels (USVs) to improve cooperative tracking safety in complex marine environments. It uses a prescribed performance control method with adjustable boundaries to minimize tracking errors and reduce collision risks, while an integral disturbance observer addresses model uncertainty and external disturbances. A control barrier function (CBF) ensures safety by solving a quadratic programming problem, balancing formation tracking and security. Simulations show the method outperforms others in response speed, disturbance rejection, and input smoothness.
190Deccan Chronicle2026-09-19
A team of students from the Indian Institute of Technology Madras (IIT-M) won the Global Autonomous Vessel Challenge held in Norway in September 2026. The competition focused on designing and operating autonomous maritime vessels. Their victory highlights India’s growing expertise in robotics and autonomous systems. The event attracted international participants from leading engineering institutions.
191Digital Ship2026-09-17
Robosys and FarSounder collaborated to enhance autonomous navigation systems with underwater perception technology. The integration aims to improve situational awareness for unmanned vessels. This advancement supports safer and more efficient maritime operations. The partnership was announced in a 2026 industry update.
192Ocean News & Technology2026-09-14
Robosys Automation has integrated its VOYAGER AI platform with FarSounder’s sonar technology to enhance autonomous navigation for vessels. The collaboration aims to improve obstacle detection and situational awareness in maritime environments. This integration combines AI-driven decision-making with advanced sonar capabilities. The system is designed to support safer and more efficient autonomous operations at sea.
193Unmanned Systems Technology2026-09-01
Cambridge Pixel will demonstrate software and sensor integration for autonomous vessels in an upcoming showcase. The technology focuses on enhancing navigation and operational capabilities for unmanned maritime systems. The event highlights advancements in autonomous vessel control and sensor data processing. No specific technical details or outcomes are provided.
This paper presents a formation control method for autonomous surface vehicles (ASVs) in waterways with currents, obstacles, and boundaries. The approach uses multi-agent navigation functions, allowing ASVs to maintain relative positioning while drifting with currents instead of resisting them. The method was tested in simulations and real-world experiments with four micro-ASVs in a lake and river, handling tidal currents, GPS errors, and communication dropouts. Results showed successful formation convergence and collision avoidance, even after temporary communication loss.
This study introduces a path planning system for Unmanned Surface Vehicles (USVs) combining Deep Reinforcement Learning (DRL) with a Control Barrier Function (CBF)-based Safety Shield to prevent collisions and comply with maritime navigation rules (COLREGs). The system uses real-time encounter classification and a CBF-QP optimization layer to filter unsafe DRL control inputs, incorporating safety radii and COLREGs constraints. A "Shielded Training" mechanism reduces reliance on the safety filter by penalizing interventions during training. Simulations show the method lowers collision and COLREGs violation rates while maintaining real-time performance.
196U.S. Naval Institute2026-08-01
The U.S. Naval Institute argues that autonomous vessel programs overlook roll stabilization as a critical specification. Current designs prioritize sensors and software but neglect the vessel's physical stability in rough seas. Without proper roll stabilization, autonomous systems may fail to perform reliably. The article emphasizes that platform stability should be a foundational requirement in development.
197Splash2472026-07-24
Davie, a Canadian shipbuilder, has launched a new division focused on autonomous vessels. The initiative aims to develop and deploy self-navigating ships in Canada. This move positions Davie as a leader in maritime automation technology. No specific projects or timelines were detailed in the announcement.
This paper introduces AIMAPP, a framework for autonomous robot navigation that combines mapping, localization, and decision-making using a single generative model inspired by animal navigation. The system builds and updates a sparse topological map in real-time, learns state transitions dynamically, and plans actions by minimizing Expected Free Energy to balance exploration and goal-directed behavior. Tested in real and simulated environments, AIMAPP operates without pre-training, adapts to sensor noise and failures, and outperforms existing planning methods. It is designed to be modular, self-supervised, and compatible with various sensors and robot configurations.
199IndexBox2026-06-09
HJSC and HD Hyundai Avikus signed a memorandum of understanding (MOU) at Posidonia 2026 to collaborate on autonomous navigation technology. The agreement aims to advance the development and implementation of self-navigating systems in the maritime industry. This partnership was announced during the Posidonia 2026 international shipping exhibition. No specific technical details or timelines were disclosed in the announcement.
200Container News2026-06-09
HJSC and HD Hyundai Avikus have partnered to develop autonomous navigation technology. The collaboration aims to advance self-navigating systems for maritime applications. This initiative reflects efforts to innovate in the shipping and vessel automation sector. No specific technical details or timelines were provided in the announcement.
201Business News Australia2026-05-26
Hyperion Systems, a Western Australian startup, revealed the southern hemisphere’s first 3D-printed autonomous vessel. The announcement marks a milestone in local maritime technology development. The vessel is designed for unmanned operations, though specific applications were not detailed. The project highlights advances in additive manufacturing for marine applications.
202Cyprus Shipping News2026-05-19
Autonomous shipping is entering a new phase, marking a shift in maritime operations. The article highlights advancements in technology enabling unmanned vessels. Cyprus Shipping News reports this development as a significant evolution for the shipping industry. No specific methods or outcomes are detailed beyond the broader trend.
203Port Technology2026-05-12
NYK and the Maritime and Port Authority of Singapore (MPA) tested autonomous vessel integration systems in May 2026. The trial focused on evaluating technologies for safe and efficient autonomous ship operations. The systems aim to enhance navigation and port integration. No specific outcomes were detailed in the report.
204rivieramm.com2026-05-01
Samsung Heavy Industries is speeding up the development of autonomous vessels. The company aims to advance its technology for unmanned and remotely controlled ships. This effort focuses on improving navigation, safety, and operational efficiency. No specific timelines or technical details were provided in the announcement.
205Tradewinds News2026-04-29
Samsung and Orca AI are integrating autonomous navigation systems into newly constructed ships. The collaboration aims to enhance maritime safety and operational efficiency. This initiative marks a step toward advanced automation in the shipping industry. The systems are being deployed in newbuildings as part of the partnership.
SafeNav is a reinforcement learning framework for autonomous mobile robots that combines a collision-aware A* path planner with a PPO-based velocity controller, linked by a safety filter using Control Barrier Functions. The system extends existing methods to handle dynamic obstacles and includes safety enhancements like a guided entropy schedule and Lagrangian constraints to limit safety violations. In 1,800 simulation trials, SafeNav achieved a 95.8% navigation success rate at 20% obstacle density, a 0.48% safety violation rate, and a 31.4% reduction in path length compared to baselines. The framework aims to improve safety, efficiency, and real-world applicability in industrial environments.
207아시아경제2026-04-07
HD Hyundai Avikus received the world’s first type approval for a universal autonomous navigation system on April 7, 2026. The certification, granted by a classification society, validates the system’s compliance with international maritime safety standards. This approval allows the technology to be installed on various vessel types without additional testing. The system aims to enhance autonomous shipping operations globally.
208Seoul Economic Daily2026-04-07
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The study introduces a new method for modeling and managing high-density bathymetric data using the S-100 framework, specifically the IHO S-102 standard. It employs a relational database and a three-level indexing system to organize and retrieve data efficiently throughout its lifecycle. The approach includes convex hull-based geometric constraints for interpolating water depth data and an HDF5-based storage model with block storage and version control, reducing storage space by 83.6%. Field tests in the East China Sea confirmed the generated S-102 data met international standards and achieved high terrain restoration accuracy.
210vosactualites.com2026-10-28
Exail, a Breton company, has developed a navigation system for naval drones that limits drift to just 1 nautical mile over 3 hours without relying on GPS. The technology aims to enhance the autonomy and reliability of unmanned maritime vehicles. This advancement targets naval applications where GPS signals may be unavailable or compromised. The system represents a strategic bet for Exail in the growing drone market.
This study presents a method for autonomous endovascular navigation using 2D image feedback from DSA and X-ray fluoroscopy. The system converts vessel images into a centerline graph, tracks device-tip progress, and uses a rule-based controller for navigation tasks like delivery and branch selection. Tested on a robotic platform with 65 runs, it achieved over 95% guidewire progress in all cases, with only 3.1% failing due to excessive stops. The approach shows promise but requires further testing with clinical images and varied anatomies.
This paper evaluates calibration methods for electromagnetic logs (EMLogs) used in autonomous surface vehicles when Doppler velocity logs (DVLs) are unavailable. It compares four calibration models using two estimation pipelines on 221 minutes of real-world data from the MARVEL vehicle. Results show the bias and scale error model with a Kalman filter improves speed estimation by 71%. Dynamic maneuvers, rather than straight-line paths, further enhance calibration accuracy, enabling real-time online EMLog calibration.
This paper introduces a new motion planning framework for complex articulated autonomous underwater vehicles (AUVs) to enable safe navigation and active perception in dynamic, uncertain environments. The method adapts an existing pipeline for holonomic AUVs to handle high-dimensional, articulated systems with unknown dynamics and multiple sensors. Simulations using an underwater snake robot and an underwater vehicle-manipulator system demonstrate real-time, efficient, and safe performance. The study also highlights computational challenges and potential improvements.
214Shipping Telegraph2026-09-03
Japan has approved the operation of an autonomous roll-on/roll-off (roro) vessel. The vessel, designed for cargo transport, marks a step forward in maritime automation. Regulatory clearance allows it to begin commercial service. This development reflects Japan’s push toward advanced shipping technologies.
This paper introduces a mixed-integer linear programming (MILP) model to automate and optimize the scheduling of Marine Autonomous Vehicle (MAV) fleets for scientific data collection. The model considers operational constraints like battery life and data collection time windows while maximizing data yield and minimizing vehicle use and energy consumption. It integrates with ship itineraries for mid-mission battery swaps or faster transit and scales efficiently, solving problems for hundreds of MAVs in minutes. The framework also supports scenario analysis and provides visualizations to aid decision-making.
This paper explores an AI-driven mooring control system for autonomous engineering vessels, replacing manual winch adjustments with an adaptive neural feedback law. The method compensates for unknown vessel dynamics and environmental disturbances in real-time without prior model identification. Simulations show the neural controller achieves steady-state positioning accuracy comparable to model-based designs, while a reinforcement learning (PPO) approach performs similarly to a PD regulator but with less precision. The system is proven stable (SGUUB) and respects physical constraints like line tension limits.
217NavalToday.com2026-07-24
Saronic and Samsung Heavy Industries have formed an alliance to develop autonomous maritime systems. The partnership aims to advance technology for uncrewed and remotely operated vessels. No specific projects or timelines were disclosed in the announcement. The collaboration focuses on innovation in the maritime industry.
The paper introduces TerrainFormer, a framework combining a world model for terrain prediction with a decision transformer for off-road navigation. It uses self-supervised pretraining on LiDAR data to learn terrain features, followed by behavioral cloning for action selection. The model achieves 87.31% test accuracy and 0.7948 macro F1 on the RELLIS-3D dataset, with strong cross-dataset generalization. Predicted future frames show minimal accuracy loss (0.79%) compared to ground-truth observations.
This paper introduces an intelligent guidance system for autonomous vessels, combining traffic rule awareness and semantic reasoning to enable collision-free navigation. The framework uses qualitative traffic rule data, environmental information, and sensor inputs to make real-time path planning decisions. A finite-state machine assigns traffic roles, while a path planner calculates safe routes considering vessels and infrastructure. The approach was validated in case studies involving head-on and crossing scenarios in short-sea and inland waterways.
This paper introduces a framework that jointly optimizes target perception and disturbance estimation for unmanned surface vessels (USVs). It combines a hybrid CNN-Transformer model for robust visual feature extraction, a reinforcement learning-tuned sliding mode observer for adaptive disturbance estimation, and a confidence-guided strategy to synchronize perception and observer performance. Tests in simulations and outdoor lake experiments show improved accuracy, convergence, and stability over traditional decoupled methods. The study validates initial real-world performance but notes the need for broader open-water and multi-vessel testing.
This paper introduces a falsification-driven reinforcement learning (RL) method to improve rule compliance in autonomous maritime vessels. The approach generates adversarial training scenarios where vessels violate maritime traffic rules, expressed as signal temporal logic specifications. Experiments on open-sea navigation with two vessels show the method produces more relevant training scenarios and enhances consistent adherence to traffic rules. The study highlights the use of temporal logic robustness measures to extend maritime traffic rules.
This paper reviews how surface mine planning must adapt to integrate Autonomous Haulage Systems (AHS) in open-pit metal mining. Key changes include workforce dynamics, communication infrastructure, and haul road design, with debates on optimal road width and load channelization. The study emphasizes the need for holistic, data-driven mine design and better human-autonomous collaboration. Empirical validation of workforce transition strategies is also highlighted for safer and more effective AHS deployment.
This paper presents an autonomous navigation system for a split-type flying vehicle's intelligent chassis, using LIDAR and IMU for positioning and the A* algorithm for path planning. The system, tested in simulations and closed-field experiments, achieved stable navigation to preset waypoints with sufficient accuracy. Additionally, the study designs a mechanical slide rail docking structure, validated through finite element analysis, showing minimal deformation under load and meeting stiffness and stability requirements. The work supports modular operations for combined air and ground transport modes.
This paper presents a mission-planning framework for autonomous marine vehicles conducting seabed surveys, focusing on safety, COLREG compliance, and operator transparency. It introduces a layered architecture for an onshore-supervised surface vessel that generates coverage plans from operator-defined polygons and produces structured reports for underwater vehicle inspections. The system includes a behavior-tree executive, a reinforcement-learning advisor for optimized survey edits, and a risk assessment layer for collision avoidance. The framework is validated through ongoing development of a surface vessel and remote operation center, using repeatable logs and status reporting.
This paper presents a 2-DOF tendon-driven manipulator for aquatic Autonomous Surface Vehicles (ASVs), such as the MallARD platform, to expand their operational range for both underwater and surface tasks. The design places actuators above the waterline, using wires to power the joints, allowing operation in high-radiation environments, handling heavy payloads, and resisting water exposure. A real-world prototype was built and tested, demonstrating feasibility with joint angle errors below 2.1° RMS. The inverse kinematics were solved analytically, and experimental results confirm its potential for underwater applications.
This paper introduces an adaptive fuzzy PID controller to improve trajectory tracking in wheeled mobile robots (WMRs) operating in unstructured environments. The controller addresses challenges like nonlinear dynamics, wheel slips, actuator noise, and external disturbances using a cascaded structure with real-time parameter adaptation. Simulations on complex lemniscate curves show reduced RMS tracking error compared to conventional PID and adaptive dynamic control methods. The approach demonstrates robustness against parameter variations and disturbances while maintaining computational efficiency for real-time applications.
227Naval News2026-11-04
Zodiac Milpro launched the Equinox 930, its first autonomous, modular uncrewed surface vessel (USV), at Euronaval 2026. The 9.3-meter aluminum USV supports multi-mission roles like surveillance, mine countermeasures, and hydrography, with speeds over 40 knots and a range exceeding 600 nautical miles. Its design emphasizes modularity, interoperability, and adaptability, allowing integration with various autonomy systems and existing naval assets. Production spans five global sites, with Zodiac Milpro also offering maintenance and training services.
This study developed an autonomous tracked robot for spraying in sheep houses, using a three-level navigation system with ROS to handle challenges like narrow aisles and animal blockages. Simulations showed high mapping accuracy (96.8% intersection-over-union) and precise localization (RMSE of 0.0299 m), while physical tests achieved near-complete coverage (99.8%) with minimal overlap. Spray deposition rates remained consistent across speeds (0.1985–0.2008 L/m²), though flow rate increased with speed. Field trials demonstrated reliable path tracking (RMSE 0.0376 m) and effective handling of animal blockages (55/56 events resolved automatically).
This paper presents a method using model predictive control to maintain stable hydroacoustic communication between an AUV and a support vessel or USV. The approach optimizes vessel positioning by predicting AUV trajectories, vessel dynamics, and hydroacoustic channel conditions to maximize signal-to-noise ratio (SNR). Simulations showed the method prevented communication loss and reduced unnecessary vessel movements compared to alternatives. The technique was validated as effective for reliable AUV operations in open sea conditions.
This paper presents a multi-agent GeoAI system for Arctic eco-navigation that balances vessel safety, efficiency, ecological protection, and community impacts. The system integrates operational, physical, ecological, and social criteria, using specialized agents to process geospatial data, generate multi-objective routes, and provide decision support. It explicitly accounts for sensitive habitats like Essential Fish Habitat and seal critical areas while keeping human oversight for value-based decisions. The framework aims to improve transparency and social responsibility in Arctic maritime routing.
This paper presents a control framework for autonomous berthing and unberthing of a 5000-ton inland cargo ship, addressing challenges like environmental disturbances, precise attitude control, and energy efficiency. A Lyapunov-based controller with time-varying gains and sliding-mode terms stabilizes roll, pitch, and yaw within 10–15 seconds in simulations under strong wind, wave, and current disturbances. The system integrates SLAM mapping, visual recognition, and dynamic obstacle avoidance to generate safe, smooth, and energy-optimal trajectories. Compared to traditional methods, it reduces control energy consumption while providing theoretical support for real-world applications.
This paper introduces a Quantum Game-Theoretic Reinforcement Learning (QGTRL) framework for autonomous vehicle (AV) navigation and communication in dynamic networks. The approach combines quantum-inspired policy encoding with game-theoretic coordination to improve decision-making, exploration efficiency, and scalability. Benchmarked against methods like DQN and MADDPG, QGTRL achieves 18.7% better path-planning accuracy, 15.3% lower communication latency, and 22.4% reduced energy consumption. The framework operates on classical hardware, making it practical for real-world deployment in cooperative AV systems.
This paper presents an adaptive predefined-time sliding mode control (APTSMC) strategy for unmanned surface vehicles (USVs) to improve trajectory tracking and collision avoidance in maritime sports support. The method uses a predefined-time sliding surface and adaptive compensation to ensure predictable convergence and disturbance rejection without prior knowledge of disturbances. A velocity-modulated artificial potential field corrects paths for obstacle avoidance, adjusting repulsion based on proximity. Simulations demonstrate the framework’s effectiveness in balancing tracking accuracy and safety in dynamic environments.
234spsnavalforces2026-11-05
Navantia presented its next-generation naval capabilities at Euronaval 2026, focusing on the naval combat cloud, F110 IAMD frigate, and LASV-75 autonomous surface vessel. The company emphasized digital integration, autonomous systems, and enhanced combat capabilities to address evolving threats and operational demands. Key advancements include the SCOMBA NG combat system, SPY-7 radar, and modular autonomous platforms designed for hybrid fleet operations. Navantia also strengthened European cooperation through agreements with Naval Group and Fincantieri for the European Patrol Corvette program.
235oceannews2026-11-02
OceanAlpha’s L42B uncrewed surface vessel (USV) received Approval in Principle (AiP) from Bureau Veritas on July 31, 2026, after its design and operations were reviewed against autonomous shipping guidelines. The 8.51-meter aluminum catamaran is built for offshore hydrographic and geophysical surveys, supporting missions up to eight days with a 1,500 km range. It features modular payloads for seabed mapping and combines diesel-electric propulsion with remote supervision capabilities. The AiP confirms compliance with defined operating conditions and includes recommendations for further development.
236ZAWYA2026-10-21Naval News
EDGE led a UAE Navy demonstration integrating sovereign autonomous systems with U.S. Navy participation, showcasing unmanned maritime capabilities. The event featured unmanned surface vessels (USVs) performing AI-driven reconnaissance and protection missions using advanced sensors and command-and-control systems. Technologies included TACTICA AI’s Naval Warfare C2 Centre, HADAF targeting system, and platforms like the 170 M-DETECTOR vessel and SKYSHIELD-N counter-drone system. The demonstration highlighted interoperability between allied forces and EDGE’s role in advancing autonomous maritime defense.
237Hydro International2026-10-20
Sentiq Defence, founded by retired Italian Navy commander Antonio J. Belfiore, has launched as an integrator of autonomous and remotely operated maritime systems for defense, security, and blue economy sectors. The company offers uncrewed surface vessels (USVs), underwater surveillance sonars, ROVs, and aerial drones, aiming to simplify integration and provide turnkey solutions with training and support. Based in Italy, Sentiq focuses on addressing fragmented markets by tailoring commercial-off-the-shelf (COTS) systems to customer needs under the principle "command your space." Its growth plan includes expanding partnerships, building a local integration hub, and enhancing training and support services.
This study presents a control method for unmanned surface vessels facing external disturbances. A nonlinear disturbance estimator is designed to accurately estimate and compensate for disturbances in finite time. The approach combines sliding mode and backstepping control techniques to stabilize the vessel’s position and yaw angle, ensuring tracking errors converge to zero. Simulations validate the effectiveness of the proposed scheme, with stability confirmed through Lyapunov theory.
239MarineLink2026-10-09
Robosys successfully validated its VOYAGER AI maritime autonomy software. The software is designed for autonomous vessel operations in the maritime industry. The validation confirms its readiness for real-world deployment. No specific details on testing methods or outcomes were provided.
This paper details the design of an autonomous Uncrewed Surface Vessel (USV) for hydrographic surveys in shallow or hazardous waters. The USV features a catamaran hull, Brushless DC motors, and Lithium Polymer batteries for improved endurance and real-time data transmission. Computational Fluid Dynamics (CFD) simulations using OpenFOAM and static stability analysis with Paramarine software validated its hydrodynamic performance. A prototype was built and tested, confirming its stability, maneuverability, and suitability for marine research and commercial use.
241RICentral.com2026-09-04
A local company is advancing the development of autonomous vessels, positioning itself as a key player in the future of unmanned maritime technology. The initiative focuses on designing and implementing self-navigating ships for commercial and industrial use. The project aims to enhance efficiency and safety in marine operations. No specific technical details or partnerships were disclosed in the report.
This paper presents a transformer-based model for predicting future maritime radar frames using past X-band observations and vessel motion data from GNSS. The approach adapts the EchoPT framework, originally designed for simulated sonar, to real-world radar data from the MOANA dataset. The model's performance is tested against persistence and geometric warp benchmarks in both single-frame and autoregressive settings. The study also identifies failure modes tied to architectural and dataset limitations, suggesting areas for improvement.
243openalex2026-07-08Andrew Philip Single
This study advances a real-time acoustic system for autonomous underwater vehicles (AUVs) to detect and track surface vessels using a small hydrophone array. It improves prior work by handling AUV self-noise, multi-target tracking via a particle filter with DBSCAN clustering, and closed-loop motion control. Field tests in Norfolk and Claytor Lake validated the system, while simulations confirmed stable tracking of one or two acoustic sources under varying conditions. A rear suppression tool was found essential for reliable performance when AUV self-noise interfered with detection.
The ROBBE system is an autonomous robot designed for coating removal and decontamination of steel components in nuclear dismantling, using real-time 3D scanning, digital-twin trajectory planning, and ultra-high-pressure waterjet technology. It replaces manual labor, reducing radiation exposure and improving safety by operating in a closed cabin with optimized tool paths and process parameters. After 12 months of testing, ROBBE achieved over 90% decontamination on most parts in under 30 minutes, demonstrating reliability and efficiency. The system is now at technology readiness level 7–8 and is being expanded for broader use in nuclear decommissioning.
This paper introduces a unified vision-based control system for autonomous wheelchairs, merging corridor following and obstacle avoidance using a single monocular camera. A convolutional neural network (CNN) predicts steering commands directly from images for corridor navigation, while image-based visual servoing (IBVS) handles obstacle avoidance by regulating detected obstacles in the image space. A supervisory controller prioritizes safety by switching between the two methods as needed. Tested on a low-cost embedded platform, the system shows stable performance in varied conditions, combining learning-based and analytical approaches for assistive mobility.
246openalex2026-04-21Alex Beaudin, Bjørn Andreas Kristiansen, Kristoffer Gryte, Corrado Chiatante +3 moreDOI ↗ (opens in a new tab) This paper introduces OLAhGP, a Gaussian process-based path planning method for robotic environmental monitoring that adapts to multi-modal sensing data while incorporating constraints. The approach optimizes future waypoints in a receding horizon framework, using the Gaussian process posterior to guide decisions. Tested on an autonomous surface vessel with oceanic algal bloom data, it outperforms existing methods by improving path informativeness and reducing misclassification errors in chlorophyll-rich waters. Results show better accuracy in bloom detection with the same number of samples.
247PressReader2026-04-07
Avikus received certification for its autonomous navigation technology in April 2026. The achievement marks a milestone for the company’s self-steering systems. No further details on the certification scope or testing process were provided. The development signals progress in maritime automation.
248Ocean News & Technology2026-11-04
Babcock expanded its ARMOR Force Framework by partnering with seven companies—CUBEDIN, Exail, Hensoldt, Kraken Technology Group, SH Defence, Stormborn, and Thales—to integrate autonomous naval technologies, including platforms, sensors, and mission systems. The framework provides navies with a unified operational system, combining crewed, lean-crewed, and uncrewed systems under a common architecture while maintaining human oversight. Babcock acts as the systems integrator, ensuring accountability, certification, and through-life support for deployable hybrid naval capabilities. The open and modular design allows for future expansion as technologies and requirements evolve.
249Unmanned Systems Technology2026-11-02
Agilica advanced to the finals of EURONAVAL 2026 by demonstrating resilient maritime autonomy and ultra-wideband (UWB) positioning technology. The system showcased enhanced navigation and operational reliability for unmanned maritime vessels. This innovation highlights progress in autonomous maritime solutions. The finals event underscores its potential impact on naval and defense applications.
This study introduces MC-PER-TD3, a reinforcement learning framework for autonomous underwater vehicles (AUVs) that prioritizes navigation balancing task completion, obstacle avoidance, communication, and energy efficiency. The method combines temporal-difference error and goal-proximity heuristics in experience replay, achieving 100% mission success with no collisions in testing. Compared to baseline models, it reduced path length and task time while improving energy efficiency, though it showed a trade-off with data throughput. The approach demonstrated robustness under noise and perturbations but had clear failure limits in extreme conditions.
This paper presents a method for safe formation control and path planning of unmanned surface vessels (USVs) in obstacle-heavy and restricted waters. It combines nonlinear model predictive control (NMPC), an adaptive virtual-obstacle replanning strategy, and a formation-transformation approach to navigate narrow gaps and reconfigure vessel arrangements. Simulations in MATLAB and real-USV experiments validate the framework’s ability to handle dynamic obstacles, narrow waterways, and multi-vessel formations. The approach adjusts obstacle parameters iteratively to ensure collision-free trajectories while maintaining formation stability.
This paper presents a hybrid control system for unmanned surface vessels (USVs) combining improved deep deterministic policy gradient (IDDPG) and model predictive control (MPC). The IDDPG algorithm generates optimal heading commands, while MPC optimizes thrust and rudder angles using a nonlinear dynamics model. Simulation results show a 37% reduction in lateral deviation and a 21% decrease in heading angle error compared to traditional ALOS-PID methods. The approach enhances path-tracking precision in complex marine environments.
This study introduces a hybrid control system for unmanned surface vessels (USVs) combining improved deep deterministic policy gradient (IDDPG) and model predictive control (MPC). IDDPG generates optimal heading commands using reinforcement learning, while MPC optimizes thrust and rudder angles based on a nonlinear dynamics model. The method reduces average lateral deviation by 37% and heading angle error by 21% compared to traditional ALOS-PID control. Simulation results confirm improved adaptability and precision in complex marine environments.
This study introduces a hybrid control system for unmanned surface vessels (USVs) combining improved deep deterministic policy gradient (IDDPG) and model predictive control (MPC) to enhance path-following in marine environments. The IDDPG algorithm generates optimal heading commands, while MPC optimizes thrust and rudder angles using a nonlinear dynamics model. Simulation results demonstrate a 37% reduction in average lateral deviation and a 21% decrease in heading angle error compared to traditional ALOS-PID methods. The approach improves adaptability and precision in complex, dynamic conditions.
This study introduces a hybrid control method for unmanned surface vessels (USVs) combining improved deep deterministic policy gradient (IDDPG) and model predictive control (MPC). The IDDPG algorithm generates optimal heading commands, while MPC optimizes thrust and rudder angles using a nonlinear dynamics model. Simulations show the method reduces average lateral deviation by 37% and heading angle error by 21% compared to traditional ALOS-PID control. The approach improves adaptability and precision in complex marine environments.
The paper introduces FFRLS-MPC, an adaptive model predictive control method for ship course keeping that combines online system identification with MPC. It uses recursive least squares with a forgetting factor (FFRLS) to update key maneuvering-model parameters in real time, improving state prediction and rudder optimization. Simulations under calm and disturbed conditions showed FFRLS-MPC outperformed PID and robust adaptive MPC baselines, achieving faster responses, lower errors, and better dynamic tracking. The method effectively compensates for model inaccuracies and environmental disturbances.
This paper introduces RMP-Nav, a reactive local planner for differential-drive robots that uses Riemannian Motion Policies and hierarchical wavemaps for autonomous exploration. The method reduces local-planning runtime to under 0.1 ms per call, significantly faster than the optimization-based FALCO planner, while maintaining comparable waypoint-completion performance in open environments like Campus and Forest. It also improves obstacle clearance and reduces jerk but shows moderate performance drops in constrained spaces like garages. The findings suggest reactive control is effective in geometry-heavy settings, though optimization-based approaches may still excel in topologically complex areas.
258Tech Times2026-10-20
HD Hyundai and Anduril demonstrated full-ship autonomy in Long Beach, moving beyond basic navigation capabilities. The test proved advanced autonomous systems can control an entire vessel, not just its movement. The companies showcased the technology’s potential for maritime operations. The event marked a step toward broader adoption of autonomous shipping.
259Marine Insight2026-10-13
China Merchants Energy Shipping has partnered with RINA to investigate AI-driven technologies for ship operations. The collaboration aims to enhance efficiency and safety in maritime activities. The initiative will explore the use of artificial intelligence in optimizing vessel performance. The project was announced on October 13, 2026.
This study introduces a method to assess ship conflicts in confined inland waterways using AIS data by analyzing ship behavior and maneuvers. It filters geometry-induced turning behavior and links ship-domain models to observed avoidance actions, optimizing parameters for crossing and overtaking encounters. Analysis of 10,256 trajectories from the Yangtze River revealed distinct spatial patterns: crossing conflicts clustered in two regions, while overtaking conflicts spread along the main channel. The findings offer actionable insights for improving safety management in inland waterways.
This study applies Model Predictive Control (MPC) to unmanned surface vessels, using a predictive model that adjusts for water depth and speed variations. Parameters were identified through free-running tests and estimated via nonlinear least squares. A self-tuning method dynamically adjusts the MPC’s prediction horizon to suit different conditions. Simulations across 25 scenarios, varying under-keel clearance and speed, confirmed the controller’s high path-following accuracy and adaptability.
This paper reviews technologies and challenges for autonomous Crew Transfer Vessels (CTVs) and Service Operation Vessels (SOVs) in offshore wind farms. It highlights gaps in current manual operations, limited validation of autonomous systems, and the potential for improved efficiency, safety, and cost reduction. Key advancements in sensors, automation, and risk assessment are discussed, along with operational hurdles. The authors outline research priorities to advance autonomous vessel development.
263crossref2026-09-01Irene Mejía Hurtado, Juan Camilo Angarita Noriega, Enric Cervera Mateu, Raúl Marín PradesDOI ↗ (opens in a new tab) This study compares Nav2 and EasyNav for LiDAR-inertial localization in land and maritime robots using a shared Livox MID-360 LiDAR and NVIDIA Jetson Orin Nano setup. Both frameworks process data with Fast-LIO2 for odometry and point cloud reconstruction, but Nav2 uses online 2D costmaps while EasyNav relies on a pre-built occupancy map. In port tests, EasyNav delivered more consistent positioning, whereas Nav2 showed higher sensitivity to local perception changes. The work highlights differences in how each framework handles perceptual data integration.
264Chosunbiz2026-07-24
South Korea is supporting its shipbuilding industry in adopting domestically produced green and autonomous equipment. The government aims to boost the sector's competitiveness by encouraging the use of eco-friendly and self-operating technologies. This initiative is part of broader efforts to advance the country's maritime and shipbuilding capabilities. The focus is on reducing emissions and improving efficiency through innovation.
The paper explores basing and deployment methods for unmanned surface vehicles (USVs) on ships, relying on foreign media reports. Advances in automatic launch and recovery systems (LARS) enable USV operations, though their development poses technical challenges. Eleven LARS types are discussed, with designs varying by vessel type, USV weight, and placement (deck or hull). The most advanced systems can function in sea conditions up to 6 points on the wave scale.
266Marine Log2026-05-28
The Seasats Lightfish unmanned surface vessel (USV) completed its first autonomous transit of the Taiwan Strait in May 2026. During the mission, it encountered a Chinese corvette. The event marks a notable development in autonomous maritime navigation. No further details on interactions or outcomes were provided.
267Interesting Engineering2026-05-19
The Comet autonomous warship is designed for military use, capable of carrying a 10,000-pound payload, including missile strikes. It can reach speeds of up to 45 knots. The vessel operates without a crew, relying on autonomous systems. Details were published by Interesting Engineering in May 2026.
This paper examines a heading stabilization system for small vessels using a nonlinear robust RISE controller to counteract external disturbances like wind, waves, and currents. The study employs VeSim simulation software to model vessel dynamics and optimize the controller’s tuning parameters under various environmental conditions. Optimal gain coefficients were determined for different disturbance scenarios, creating a database for adaptive controller adjustments. Artificial intelligence methods, such as neural networks, are suggested for selecting parameters based on real-time environmental data.
269Smart Maritime Network2026-11-06
DP World will partner with SHIFFT, a Dubai Future Foundation-led joint venture, to develop autonomous logistics systems for ports and industrial zones, starting at Jebel Ali Port. The collaboration aims to integrate autonomous vehicles into operations, focusing on safety, fleet management, traffic control, and digital twin technologies across DP World’s global network of over 60 ports. The initiative, supported by autonomous vehicle firm Oxa, seeks to enhance supply chain reliability and workforce skills while improving operational efficiency. Dubai’s Research, Development and Innovation Programme will oversee the project, targeting scalable solutions for global deployment.
270아시아투데이2026-10-28
South Korea is introducing AI across marine and fisheries sectors to improve efficiency. The initiative includes deploying unmanned vessels for operations. AI will also be used to predict seafood supply and demand. The technology aims to modernize and optimize the entire industry.
271국민일보2026-10-25
South Korea’s shipbuilding industry is undergoing regional restructuring, with Busan focusing on eco-friendly technologies, South Gyeongsang prioritizing AI integration, and Ulsan specializing in autonomous navigation. Each region is aligning its efforts to strengthen competitiveness in different sectors of the industry. The shift aims to optimize resources and expertise across the country. The restructuring initiative was reported by *국민일보* on October 25, 2026.
272데일리뉴스2026-10-17
The article highlights a 1.4 trillion won initiative in shipbuilding, emphasizing the critical role of "data connection" in integrating AI across the entire process. This includes linking design phases with robotic systems and autonomous navigation technologies. The focus is on creating seamless data flow to enhance efficiency and innovation in the industry. No specific methods or outcomes beyond the emphasis on data integration are detailed.
This paper introduces a cross-modal semantic–geometric filtering method for LiDAR SLAM in dynamic water-surface environments, addressing issues like moving vessels and reflection-induced noise in USV mapping. The approach combines visual semantic data with LiDAR point confidence, using temporal association and adaptive attenuation to filter out interference while preserving static structures. Testing in a port-basin sequence showed an 87.18% reduction in vessel-related noise and a 77.98% decrease in reflection-affected points, alongside a 24.28% improvement in pose accuracy. The method balances noise suppression and structural preservation, enhancing trajectory consistency in dynamic conditions.
274해사신문2026-10-13
K-shipbuilding will invest 1.4 trillion KRW to advance AI transformation across its operations. The initiative extends beyond autonomous navigation to include shipyards and equipment. The company aims to expand the adoption of its M.AX AI platform. The investment reflects a broader push to integrate AI into multiple facets of shipbuilding.
275더구루2026-10-11
Hyundai Glovis will expand Avikus’ AI autonomous navigation system to 10 additional newbuild vessels, bringing the total to 18 ships. The company is increasing adoption of the technology across its fleet. No further details on implementation timelines or vessel types were provided. The expansion follows prior installations of the AI navigation system.
This paper introduces a minimal developmental intelligence model for autonomous robots, reducing reliance on predefined knowledge by distinguishing essential developmental mechanisms from learnable content. The system achieves comparable performance with only 2 preset task rules versus 8 in a richer adaptive system (0.633 vs. 0.632) and outperforms a short-memory baseline in delayed relation accuracy (0.921 vs. 0.504). It also autonomously discovers hidden features and interactions, reaching a held-out AUROC of 0.899 compared to 0.704 for the base model. The approach emphasizes long-term learning retention and outcome-guided knowledge reuse.
277한국일보2026-09-30
The article discusses advancements in AI, eco-friendly, and autonomous navigation technologies to lead the future ship market. It highlights efforts to strengthen these technologies in South Korea. The focus is on improving efficiency and sustainability in maritime operations. The developments aim to position the country as a leader in next-generation shipbuilding.
278빅데이터뉴스2026-09-29
HMM plans to apply AI-based autonomous navigation across its entire fleet, aiming to emulate Tesla’s self-driving technology but for ships. The initiative seeks to enhance operational efficiency and safety by integrating advanced AI systems. The project reflects the company’s long-term vision for smart shipping solutions. Implementation is expected to begin in phases starting in 2026.
279www.ebn.co.kr2026-09-25
Autonomous navigation technology is now deployed on 700 commercial vessels, marking a shift toward AI-driven maritime operations. The advancement suggests AI could increasingly replace traditional ship captains in managing navigation. This development reflects broader trends in automation within the shipping industry. The article highlights the growing commercial adoption of these systems.
This paper introduces a cost-effective method for autonomous mobile robots to locate and dock with transport trolleys using a single LiDAR sensor. The system identifies trolley legs within predefined detection zones and enables docking without requiring precise robot positioning, utilizing an ESP32-based control system and ROS framework. Experiments confirmed successful trolley detection and docking when the robot was positioned in front of the target under controlled conditions. The approach aims to improve automated transport systems in warehouses and manufacturing facilities.
This paper introduces an adaptive path planning algorithm for unmanned surface vehicles (USVs) to improve navigation in dynamic maritime settings. The method combines an enhanced ant colony optimization (ACO) algorithm—using a pheromone penalty mechanism and path smoothing—with a modified dynamic window approach (DWA) that optimizes overshoot, selection efficiency, and adaptive weight adjustments. Simulations show the approach reduces travel length and improves collision avoidance compared to existing methods. The result is more reliable navigation in complex marine environments.
This paper introduces a parallel thinking-trace-guided auto-learning model for autonomous robots to improve efficiency in handling routine tasks. The system includes a main thinking system for complex scenarios, an Auto Learning and Decision System (ALDS) that converts thinking traces into executable models, and an Obvious Learning system (OBL) for storing abstract rules. Testing showed the model improved F1-scores, reduced main-thinking invocations by 80.31%, and cut latency by 75.34% while maintaining a 0.908 task success rate. Error-triggered rethinking also significantly boosted performance in conflict scenarios.
283openalex2026-07-02Aswin Ramachandran, Christopher Golling, Sebastian Burmester, Noa Sendlhofer +3 moreDOI ↗ (opens in a new tab) This paper introduces a computational framework, *Way of Water*, for choreographing autonomous surface vessels in open water, addressing challenges from nonlinear fluid dynamics. The system includes *Way of Water Studio*, a browser-based tool that allows artists to design music-responsive choreography using a visual timeline and advanced control algorithms. The framework integrates a custom holonomic chassis, real-time state estimation, and LTE/MQTT communication with RTK-GPS synchronization. It was validated through performances at Lake Zurich and the Venice Biennale, demonstrating its potential for robotic aquatic art.
284Hellenic Shipping News2026-06-26
The Seawork 2026 conference highlighted advancements in autonomous maritime technology. Industry leaders discussed the latest developments and future trends in shipping innovation. Awards were presented to recognize outstanding contributions in the sector. The event emphasized the growing role of automation in maritime operations.
This study presents a multi-objective MILP model to optimize bidirectional ship scheduling in multi-chamber locks, aiming to reduce ship waiting time and boost chamber utilization. A custom adaptive large neighborhood search (ALNS) algorithm was tested using real-world data from Huai’an Lock, showing a 12.98% increase in chamber utilization and a 44.40% reduction in waiting time. The ALNS method outperformed greedy heuristics, genetic algorithms, and particle swarm optimization in efficiency. The research also suggests that flexible directional control strategies improve performance under uneven traffic demands.
Nicolò Colombaro’s 2026 paper compiles *Series III of The Vessel Problem*, a 19-part study on the technical and civilizational steps needed for sustainable interstellar travel. It covers propulsion, navigation, braking, infrastructure like harbors and shipyards, and recursive systems for long-term growth, testing designs through falsification. The work frames interstellar travel as an evolving ecosystem where ships, factories, and memory systems prepare destinations for habitability. It serves as an open research framework, outlining questions and challenges rather than offering final solutions.
This paper presents the Finite Direction Hopfield Neural Network (FDHNN), a new method for autonomous pathfinding in underactuated Autonomous Underwater Vehicles (AUVs). The approach uses eight directional neurons per grid cell with tailored connection weights to address challenges like limited sensor field of view and directional constraints. It combines global planning via FDHNN with a refined Dynamic Window Approach (DWA) for local maneuvering. Simulations show improved performance over existing algorithms, enhancing reliability in underwater navigation.
This paper presents a framework for certifying the safety of autonomous inland vessels by integrating sensor availability, perception reliability, and adaptive control. It uses a pre-computed lookup table to manage degraded sensors, reducing computational load while ensuring predictable behavior. The approach combines redundancy, reliability validation, and risk-based control to support safe navigation under varying conditions. The method aims to meet certification requirements for autonomous systems.
The paper introduces an optimized REINFORCE algorithm using strategic gradients to improve visual navigation for autonomous driving robots. The enhanced model achieves 95.7% accuracy and 91.2% recall, outperforming comparative methods. In simulations, the algorithm reaches a 92.5% navigation success rate, exceeding traditional methods by 15.8%. Real-world tests show a 20.3% reduction in navigation decision time.
This paper addresses cybersecurity threats to unmanned surface vehicles (USVs), focusing on false data injection (FDI) attacks that disrupt sensor data or communications. It introduces an adaptive control scheme with an adaptive threshold event-triggering mechanism (ATETM) to maintain stability and reduce communication demands. Novel coordinate transformations and the Nssbuam function help counteract time-varying attack effects. Simulations confirm the method achieves accurate path tracking while lowering control update frequency under deception attacks.
This study introduces an LSTM-MPC control framework to improve velocity stabilization in Autonomous Underwater Vehicles (AUVs) under unpredictable marine conditions. The approach uses an LSTM neural network to predict velocity and integrates it with Model Predictive Control (MPC) to optimize performance while accounting for actuator limits. An online learning mechanism allows real-time adaptation to disturbances and sensor noise without relying on analytical models. Experimental results show the method outperforms PID-based controls, reducing tracking errors in rotational axes while maintaining precision in translational movements.
This study introduces a deep-learning model combining multi-graph convolutional networks and gated recurrent units to predict maritime vessel traffic flow in inland waterways. The approach uses three unstructured graphs to capture spatio-temporal relationships between traffic locations, improving feature extraction. Tested on two real-world datasets, the model outperformed conventional methods in both regional and station-based predictions. The results suggest enhanced accuracy and reliability for waterway management and lock scheduling.
This paper introduces a dynamic obstacle-avoidance algorithm for unmanned surface vehicles (USVs) by merging Fuzzy-enhanced Dynamic Window Approach (FDWA) with an Improved Bidirectional A*–Improved Grey Wolf Optimizer (IBA*–IGWO) framework. The method improves traditional DWA by optimizing the initial heading angle and using a fuzzy controller to adjust weight coefficients in the cost function, enhancing adaptability in varying environments. The IBA*–IGWO algorithm provides a global route, with key nodes serving as local endpoints for FDWA to ensure optimal navigation and dynamic adjustments. Simulation results confirm the method’s effectiveness in avoiding unknown and dynamic obstacles while returning USVs to pre-planned routes.
This paper presents a real-time Model Predictive Control (MPC) approach for optimizing energy management in autonomous electric vehicles (AEVs) under cold temperatures. It addresses the challenge of balancing battery power allocation between propulsion, HVAC systems, and battery preconditioning to preserve driving range and battery health. The method dynamically adjusts power distribution to maintain thermal comfort while ensuring the battery is ready for immediate charging upon arrival. The study aims to replace heuristic rules with a principled strategy for improved energy efficiency.
The paper *BIND-USBL* introduces a cooperative localization system for Autonomous Underwater Vehicles (AUVs) in GPS-denied environments, where drift from inertial navigation is a major issue. The framework uses Autonomous Surface Vessels (ASVs) with Ultra-Short Baseline (USBL) acoustic positioning to provide intermittent fixes, reducing dead-reckoning errors. Key components include a multi-ASV formation model, a conflict-graph-based scheduler for efficient acoustic updates, and delayed fusion of USBL data with inertial measurements. Simulations demonstrate improved localization performance based on survey scale, acoustic coverage, and ASV-AUV team geometry.
This paper explores a method to reduce computational demands in autonomous vehicle steering control. Traditional robust controllers for lane-keeping systems often result in high-order computations, straining processing resources. The authors propose an interlacing technique to simplify the controller’s implementation, lowering computational load while maintaining performance. The approach is tested on real path-tracking scenarios, demonstrating its effectiveness for state-space and MIMO controllers.
This paper introduces a multi-layer formation control method for unmanned surface vehicles (USVs) to improve pursuit–evasion tasks in dynamic maritime environments. The approach includes a decision layer for strategic encirclement using Apollonius circles and artificial potential fields, a coordination layer with trajectory prediction via a Head-Guidance operator, and a distributed Model Predictive Control (MPC) layer for robust tracking and collision avoidance. The framework addresses uncertainties and dynamic obstacles, with performance validated through simulations and a real-water experiment using a USV prototype. Results confirm its effectiveness under complex marine conditions.
This dataset presents simulation results for a Lyapunov-constrained Soft Actor-Critic hierarchical control system applied to unmanned surface vessels. It covers performance under Sea State 3 and 4 conditions, including tracking responses, control inputs, actuator states, and environmental disturbances. The data supports findings in an associated manuscript by Zou, Qi, Wu, and Li. No experimental or real-world validation is included.
This paper introduces an efficient algebraic model predictive control (e-AMPC) framework combined with a nonlinear disturbance observer (NDOB) for path-following in underactuated unmanned surface vessels (USVs) under external disturbances. The method reduces computational load using a variable coincidence-point strategy and ensures stability through Lyapunov-based analysis. Simulations demonstrate a 74% reduction in computation time compared to conventional MPC, along with improved disturbance estimation and path-tracking accuracy. The study also examines how prediction-point distribution, weighting matrices, and observer gain affect performance.
This study developed a fuzzy logic-based controller to stabilize the course of a small, high-speed unmanned boat under wind and wave disturbances. The controller’s performance was tested via computer simulations, comparing its course-keeping accuracy and rudder activity to a traditional PID controller. Key evaluation metrics included course angle stability, variance, and rudder deflection frequency. The design prioritized energy efficiency by minimizing rudder movements while maintaining precision.
301WorkBoat2026-04-14
Vestdavit, a division of FMD, has launched an autonomous recovery system designed for unmanned vessels. The system aims to improve the retrieval process of these vessels without human intervention. This development targets enhanced operational efficiency and safety in maritime operations. The technology was introduced in April 2026.
This study introduces a machine-learning model using Fermat polynomial methods (FPM) to predict ship roll angles by solving non-linear differential equations in ship dynamics. The approach transforms complex equations into algebraic forms and incorporates restoring moments and damping coefficients for FPSO and barge-like vessel models. Accuracy was validated against experimental data and the homotopy perturbation method (HPM), showing FPM as a simpler and effective alternative. Results were compared with numerical methods and real-world observations.
303shippingtelegraph2026-11-04
Singapore highlighted risks from emerging maritime technologies like AI and autonomous ships, which could improve navigation but also enable cyber threats and disruptions to critical underwater infrastructure. Minister Edwin Tong warned that drones, autonomous vessels, and cyber-attacks may compromise ship safety, port operations, or unlawfully seize vessels. The statement, delivered for Fiji, Jamaica, Malta, and Singapore, supported a mandatory framework for autonomous ships while stressing the need for accountability and safeguards. It also condemned unlawful transit restrictions in key straits, citing global economic risks.
304UK Defence Journal2026-10-29
Leonardo will integrate Shield AI’s Hivemind autonomy software into the Proteus uncrewed rotorcraft, developed with the Royal Navy and UK Defence Innovation, to enable mission-specific capabilities like anti-submarine warfare and logistics. The agreement allows Leonardo’s engineers to tailor and sustain the software independently, avoiding reliance on foreign suppliers. Proteus, a modular vertical take-off demonstrator, supports the Royal Navy’s 2040 aviation strategy by reducing crewed helicopter workloads. The collaboration aims to expand autonomous solutions across Leonardo’s rotary-wing portfolio.
305DredgeWire2026-10-09
Courtney Bradbury has been appointed as Vice President of Program Management at Sea Machines. She will lead the development and construction of autonomous fleets. The announcement was made by DredgeWire on October 9, 2026. Sea Machines specializes in autonomous marine technology.
306Splash2472026-10-09
The maritime industry faces escalating attacks in the Black Sea and Middle East, expanding in scale and reach. ShipGod AI, developed by Bing Hou, aims to digitize vessel operations by creating offline AI-assisted workspaces to reduce document management burdens for crews. Port New Orleans CEO Beth Branch emphasizes building capacity and flexibility to adapt to geopolitical uncertainties, while autonomous freight trains gain traction for container transport beyond port perimeters. HD Hyundai delivered the first methanol dual-fuel containership with a lashing-free system, using extended cell guides and a portable bench to eliminate manual securing, and Rio Tinto’s long-standing driverless train operations highlight automation’s role in shipping’s future.
This paper presents a control method for coordinating multiple unmanned surface vehicles (USVs) to maintain formation while tracking a virtual leader. The approach addresses challenges like unknown, changing environmental disturbances and actuator limits using a disturbance observer and dynamic compensation. Stability is confirmed through Lyapunov analysis, and simulations validate the method’s effectiveness in handling disturbances and input saturation. The system ensures obstacle avoidance while preserving formation geometry.
308bizjournals.com2026-08-31
A Rhode Island ferry company introduced an 82-foot autonomous catamaran in August 2026. The vessel is designed to operate without a crew, marking a shift toward automated maritime transport. No additional technical or operational details were provided in the announcement. The project reflects growing interest in autonomous shipping technology.
This paper presents a Particle Swarm Optimization (PSO)-based strategy for optimizing the defensive deployment of Unmanned Surface Vessels (USVs) in maritime environments. It introduces a joint optimization metric combining area and perimeter coverage of fire ranges, accounting for varying attack speeds and detection radii. Compared to Monte Carlo search, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO), PSO demonstrated competitive coverage performance with faster computation and simpler implementation. The approach balances solution quality, efficiency, and practicality for coordinated defense in complex scenarios.
310HII2026-08-11
The ROMULUS unmanned surface vessel (USV), developed by HII and MetalCraft Marine with HII Autonomy, successfully completed a critical Department of the Navy (DoW) test. The test validated the USV’s autonomous capabilities and operational performance. This milestone marks progress in advancing unmanned maritime systems for naval applications. No further technical or operational details were disclosed.
This paper presents a fault-tolerant control (FTC) method for unmanned surface vessel (USV) formations, addressing challenges like underactuation, environmental disturbances, and communication link failures. The approach uses head nodes for command dissemination and a virtual leader to generate reference trajectories, improving robustness. An adaptive state observer and backstepping-based controller compensate for actuator and communication faults in real time. Simulations show the method reduces steady-state errors to 10⁻⁴ meters and enhances fault resilience compared to non-FTC approaches.
312finance.biggo.com2026-08-06
A 2026 news article discusses an autonomous boat that successfully rescued two American pilots. The event was highlighted during a podcast featuring Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg. The article focuses on the capabilities and real-world application of autonomous maritime technology. No specific technical details or rescue circumstances are provided.
This paper introduces a new control method for unmanned surface vessels using fully actuated system theory to simplify underactuated dynamics into a decoupled form. The approach combines an extended line-of-sight guidance law with time-varying look-ahead distance to handle unknown drift, while Lyapunov analysis confirms system stability. Simulations show improved tracking accuracy, faster convergence, and better robustness than existing nonlinear methods. Future work will address parameter-dependent compensation and actuator constraints.
Daniel G. Schwartz (2026) presents new methods for unmanned surface vehicles (USVs) to avoid both fixed and moving obstacles. The approach combines a global path planner—using Grassfire, a modified Grassfire, and a new Probabilistic Roadmap variant—with a local planner that adjusts routes based on obstacle movement. The local planner prioritizes routing USVs behind moving obstacles rather than alongside them. Simulations validate the method, comparing it to the D* algorithm and other dynamic path-planning systems.
315Foro3D2026-06-30
A 2026 autonomous catamaran was wrecked after colliding with underwater debris, which damaged its composite rudder. The incident highlights vulnerabilities in the vessel’s design and navigation systems. No further details on casualties or environmental impact were provided. The failure raises concerns about the reliability of autonomous maritime technology in real-world conditions.
316Splash2472026-06-11
A fully autonomous barge successfully completed a terminal-to-terminal voyage in Rotterdam in 2026. The vessel operated without human intervention during the trip. This marks a milestone in autonomous shipping technology. The project demonstrates progress in automated maritime transport.
This paper provides three simulated marine environment maps for testing unmanned surface vessel (USV) path-planning methods. The maps feature varied obstacle layouts and were used to compare RRT*, Dubins-RRT*, and the proposed VR-LSRS-RRT* algorithm. Performance was measured in solution time, path length, smoothness, convergence, and tracking accuracy. The files are shared to ensure reproducibility of the study’s simulation conditions.
318Victoria News2026-04-07
A company plans to introduce an autonomous ferry service to connect communities in Greater Victoria by 2026. The proposed system aims to improve transportation efficiency and reduce reliance on traditional ferry routes. No specific technical details or operational timelines beyond the vision have been confirmed. The project is still in the conceptual phase.
The paper presents a zero-sum game-based adversarial control method for unmanned surface vessels (USVs) using a fully actuated system approach (FASA) to handle disturbances. A reinforcement learning algorithm with an actor-critic architecture and policy iteration is introduced, eliminating the need for precise system dynamics through off-policy learning with neural networks. The study shows that the evaluation function converges toward optimality while neural network weights remain bounded. Simulation results validate the effectiveness of the proposed approach.
320강원도민일보2026-11-08
The *Tamnara-ho*, a passenger ship operated by Namisum, became South Korea’s first domestic vessel to receive approval for its autonomous operation safety assessment. The certification confirms the ship meets safety standards for unmanned or partially automated navigation. This milestone reflects progress in integrating autonomous technology into maritime transport. The assessment was conducted under domestic regulatory frameworks.
321연합뉴스2026-11-08
The passenger ship *Tamnara-ho* operating near Namisum Island in Chuncheon has been approved for an autonomous operation safety assessment. This marks a step toward evaluating the vessel's capability for self-navigation. The approval was announced by Yonhap News on November 8, 2026. No further details on the assessment timeline or process were provided.
322재경일보2026-11-08
The *Tamnara-ho*, a passenger ship operating near Namisum Island, received South Korea’s first domestic safety approval for autonomous operation. This marks a milestone in the country’s maritime industry for autonomous vessel technology. The approval confirms compliance with safety standards for unmanned or partially automated navigation. No further technical or operational details were provided in the report.
323MarketScale2026-11-06
Burq introduced *Earned Autonomy*, a framework where delivery AI starts with no control and gradually gains authority per shipping mode (e.g., fleet, gig, parcel, LTL, drones) based on live-order performance, such as 90% dispatcher agreement. Retailers set trust thresholds, and AI is automatically demoted if it breaches hard rules, ensuring gradual, evidence-based autonomy. The system tracks decisions in a *Decision Ledger*, recording each order’s outcome, but lacks pre-live performance metrics. Burq’s approach contrasts with "human in the loop" models, treating dispatchers as temporary validators rather than permanent overseers.
324note2026-11-06
Shield AI has secured $400 million in funding for its AI-piloted X-BAT unmanned fighter, which takes off and lands vertically without a runway. The first full-scale prototype was completed in November 2026, with flight tests scheduled by year-end, though the aircraft has not yet flown. The U.S. Navy and Shield AI are funding development in stages, targeting initial operational capability by 2029. The company’s smaller V-BAT drone is already in use by the U.S. Coast Guard, Japan’s Maritime Self-Defense Force, and Ukraine, supporting its credibility.
325台灣英文新聞2026-11-05
The article discusses how Ukraine’s unmanned boat warfare experience could inform Taiwan’s asymmetric defense strategies, though direct application is limited due to differing sea conditions, communication challenges, and defense environments in the Taiwan Strait. The DSET report suggests trilateral cooperation (Taiwan-U.S.-Ukraine or Taiwan-Japan-Ukraine) to combine Taiwan’s manufacturing strengths with Ukraine’s software and combat expertise, while addressing export controls and intellectual property concerns. While Ukraine’s low-cost, high-impact unmanned boats have proven effective in the Black Sea, Taiwan’s harsher maritime conditions and lack of combat experience may reduce their immediate effectiveness. The report emphasizes the need for localized testing, modular development, and cost-benefit analysis before scaling procurement.
326北海道新聞デジタル2026-11-03
Tsuneishi Solutions Tokyo Bay obtained ClassNK technical certification for its autonomous navigation technologies, "sMCS" and "iRPS." The certification confirms compliance with industry standards for these systems. The technologies aim to enhance ship navigation and operational safety. The announcement was made on November 3, 2026.
327硬是要學 SOFT4FUN2026-11-03
Taiwan’s "Feng Jin Mo Gui Yu" unmanned surface vessel, developed by CSBC, debuted at the 2026 MATURE Taiwan expo. The 8.6-meter trimaran features stealth design, AI target recognition, and a top speed of 35+ knots, with capabilities for asymmetric warfare and port security. It has accompanied Taiwan’s Hai Kun submarine in sea trials and supports swarm operations of up to 50 vessels per control station. While still a prototype without government contracts, CSBC estimates potential annual production of 40–500 units.
328ライブドアニュース2026-10-30
Tsuneishi Solutions Tokyo Bay received ClassNK’s technical certification (TQ) for its autonomous navigation technologies, "sMCS" and "iRPS," on October 30, 2026. The certification recognizes the technical reliability of these systems. "sMCS" and "iRPS" are designed to support autonomous ship operations. The news was reported by Livedoor News.
329沖縄タイムス社2026-10-30
Tsuneishi Solutions Tokyo Bay received ClassNK’s technical certification (TQ) for its autonomous navigation technologies, "sMCS" and "iRPS." The certification recognizes the systems' compliance with industry standards for maritime safety and performance. These technologies aim to enhance ship navigation through automation. The achievement was reported by Okinawa Times on October 30, 2026.
330뉴스12026-10-28
Marine surveys are undergoing a major transformation driven by marine robots and autonomous navigation. These technologies are replacing traditional methods, improving efficiency and data collection in ocean research. The shift aims to enhance understanding of marine environments. The article highlights advancements in robotic and autonomous systems for future ocean exploration.
331연합뉴스2026-10-28
The South Korean government announced its "Marine and Fisheries AX" strategy, focusing on fully autonomous ships and robotic ports. The initiative aims to modernize maritime and fisheries infrastructure using advanced automation and robotics. The plan seeks to enhance efficiency and competitiveness in the sector. No specific implementation timeline or technical details were provided.
332뉴스핌2026-10-28
South Korea has announced an AI transformation strategy for its marine and fisheries sector, targeting global leadership in autonomous ships by 2035. The plan aims to integrate artificial intelligence into maritime operations to enhance efficiency and competitiveness. The strategy focuses on developing autonomous vessel technology and positioning South Korea as a top player in the global market. No specific methods or interim results were detailed in the announcement.
333해사신문2026-10-26
The Ministry of Oceans and Fisheries expanded maritime cooperation with Norway and India in October 2026. Discussions focused on advancing autonomous navigation technology and port development initiatives. The collaboration aims to strengthen partnerships in key maritime sectors. No specific outcomes or agreements were detailed in the announcement.
334뉴스핌2026-10-21
BON Systems successfully demonstrated the application of actuators for unmanned surface vessels in a maritime test. The company completed a real-world validation of the technology, confirming its functionality at sea. This development aims to enhance the operational capabilities of autonomous ships. The demonstration marks a step forward in unmanned maritime systems.
335財経新聞2026-10-21
HD Hyundai and Anduril demonstrated autonomous operation of an unmanned vessel off the U.S. coast in October 2026. The test included the vessel's ability to respond to an engine failure. The trial showcased advanced autonomous navigation and system resilience. No further technical details or outcomes were specified.
336뉴시스2026-10-21
BON Systems successfully demonstrated the application of actuators for unmanned surface vessels in October 2026. The company validated the technology's functionality in real-world conditions. This development aims to enhance the operational capabilities of unmanned maritime systems. No further technical details or outcomes were specified.
337라이센스뉴스2026-10-20
HD Hyundai successfully demonstrated autonomous operation of an unmanned surface vessel in collaboration with Anduril. The achievement marks a milestone in advancing maritime autonomy technology. No further technical or operational details were provided in the announcement. The project highlights cooperation between the two companies in unmanned systems.
338国际船舶网2026-10-20
Jiangsu Ocean Shipping is researching and applying full-scenario adaptive embodied intelligent ships for inland rivers. The project focuses on developing ships capable of adapting to various operational conditions autonomously. This initiative aims to enhance efficiency and safety in inland waterway transportation. The research is highlighted as part of shipowner developments in the maritime sector.
339동아일보2026-10-19
HD Hyundai and Anduril conducted the first U.S. demonstration of autonomous navigation technology for an unmanned surface vessel. The test showcased the vessel's ability to operate without human intervention. The collaboration aimed to advance unmanned maritime systems. The event took place on October 19, 2026.
340이코노뉴스2026-10-19
HD Hyundai successfully demonstrated autonomous navigation technology for an unmanned surface vessel in the U.S. on October 19, 2026. The test validated the vessel's ability to operate without human intervention. This marks a key advancement in maritime automation. The technology was showcased in a real-world environment.
341더팩트2026-10-19
HD Hyundai demonstrated the first autonomous unmanned surface vessel in the U.S. on October 19, 2026. The event marked a milestone in maritime technology, showcasing the company’s advancements in self-navigating ships. The trial highlighted the vessel’s ability to operate without human intervention. No further technical details or outcomes were disclosed.
342메트로신문2026-10-19
HD Hyundai demonstrated autonomous navigation of an unmanned surface vessel in partnership with Anduril to strengthen its U.S. market presence. The collaboration aims to accelerate HD Hyundai’s expansion in the American defense and maritime sectors. The trial showcased advanced autonomous capabilities for unmanned maritime operations. This effort reflects HD Hyundai’s strategic push into the U.S. market.
343산경투데이2026-10-19
HD Hyundai is targeting the U.S. unmanned surface vessel market and has demonstrated autonomous navigation technology in collaboration with Anduril. The partnership aims to validate and advance the company’s capabilities in unmanned maritime systems. This effort highlights HD Hyundai’s push to expand into defense and autonomous shipping sectors. The technology was proven through joint testing or development initiatives.
344나눔경제뉴스2026-10-19
HD Hyundai and Anduril conducted their first demonstration of an unmanned surface vessel (USV) in October 2026. The test successfully proved the vessel's autonomous navigation capabilities. The collaboration showcased advancements in unmanned maritime technology. No further technical or operational details were provided.
345비즈트리뷴2026-10-19
HD Hyundai demonstrated autonomous navigation of an unmanned surface vessel at sea in the U.S. in October 2026. The test verified the vessel's technology in collaboration with Anduril. The trial aimed to validate the performance and reliability of the autonomous system. No additional technical details or outcomes were disclosed.
346아시아투데이2026-10-19
HD Hyundai became the first domestic company to successfully demonstrate autonomous navigation of an unmanned surface vessel in the U.S. The trial marked a milestone for the company in advancing maritime autonomy technology. The event took place as part of efforts to expand autonomous shipping capabilities. No further technical details or outcomes were specified.
347데일리한국2026-10-19
HD Hyundai demonstrated autonomous navigation for an unmanned surface vessel in California, U.S. The event showcased the company's advancements in self-navigating maritime technology. The demonstration took place on October 19, 2026. No additional technical details or outcomes were specified.
348뉴스락2026-10-19
HD Hyundai demonstrated unmanned surface vessel operations in the U.S. to verify its autonomous navigation technology. The event showcased the company’s advancements in self-driving maritime systems. The test aimed to confirm the reliability and performance of its autonomous navigation capabilities. No additional technical details or outcomes were specified.
349파이낸셜신문2026-10-19
HD Hyundai and Anduril conducted their first demonstration of autonomous unmanned surface vessel (USV) technology in Long Beach, U.S., on October 19, 2026. The event showcased the capabilities of the jointly developed USV system. The collaboration aims to advance autonomous maritime technology. No further technical or operational details were provided in the report.
350천지일보2026-10-19
HD Hyundai demonstrated autonomous navigation technology for an unmanned surface vessel in the U.S. on October 19, 2026. The company partnered with Anduril to target the market for this technology. The demonstration aimed to showcase the capabilities of the autonomous system. The collaboration focuses on advancing unmanned maritime solutions.
351조세일보2026-10-19
HD Hyundai demonstrated its autonomous unmanned surface vessel (USV) technology in the U.S. for the first time, partnering with Anduril for verification. The trial aimed to validate the vessel's self-navigating capabilities. The collaboration focused on testing and refining the technology in real-world conditions. The event marks a step in advancing autonomous maritime systems.
352서울STV뉴스2026-10-14
Hyundai Glovis installed Avikus autonomous navigation systems on 10 of its mega carriers. The technology aims to enhance shipping efficiency and safety. This marks a significant step in adopting autonomous navigation for large-scale commercial vessels. The initiative was reported by Seoul STV News.
353手机新浪网2026-10-03
CSSC Exploration 01, a civilian marine floating test platform built by CSSC, is set to conduct sea trials. It will test shipborne unmanned helicopters, unmanned surface vehicles (USVs), and underwater vehicles. The platform will also perform engineering tests, including deck layout and electromagnetic compatibility for ships. The trials aim to validate various maritime unmanned systems and ship technologies.
354FETV2026-10-01
Samsung Heavy Industries, currently ranked lower in shipbuilding competitiveness, is focusing on autonomous navigation and robotic factories to improve its position. The company aims to enhance efficiency and innovation by integrating advanced technologies. This strategy targets long-term competitiveness in the global shipbuilding market. The approach contrasts with traditional methods used by higher-ranked competitors.
355더구루2026-09-24
HD Hyundai Avikus will supply autonomous navigation solutions for up to 32 pure car and truck carriers (PCTCs) operated by SITC. The agreement marks a significant expansion of autonomous shipping technology in commercial fleets. The solutions aim to enhance operational efficiency and safety for large-scale vessel operations. No specific implementation timeline was provided.
356헤럴드경제2026-09-15
HD Hyundai will introduce its AI-based vessel autonomy technology in the U.S. next month. The technology aims to enhance the operational capabilities of ships through artificial intelligence. The unveiling marks a step toward advanced maritime automation. No specific technical details or applications were provided in the announcement.
This paper proposes an adaptive control strategy for unmanned surface vehicles (USVs) to track trajectories under thruster saturation, unknown dynamics, state constraints, and missing velocity data. It combines signal quantization and an event-triggered mechanism to reduce communication demands, while a neural network observer estimates unavailable velocity states. The approach uses neural networks to model unknown dynamics, a low-frequency gain method to reduce signal chattering, and a dynamic auxiliary system to handle thruster limits. Stability is confirmed through Lyapunov theory, and simulations verify the method’s effectiveness.
Nazreen Rusli, Zulkifli Zainal Abidin, Muhammad Aiman Norazuddin, and Taufik Yunahar (2026) present an integrated controller system for Unmanned Surface Vehicles (USVs), designed to enhance maritime operations in environmental monitoring, resource exploration, and security. The "CxSense" controller, developed by IIUM’s Centre for Unmanned Technologies and Prostrain Technologies, meets high industry standards (IPx8, ESD, and vibration tests) for robustness and reliability. Battery-powered USVs, like those using CxSense, reduce environmental impact by eliminating direct water pollutants compared to traditional fuel-powered vessels. The system aims to improve maritime safety, efficiency, and sustainability in Malaysia and neighboring regions.
This study examines how berthing angle, water depth, and lateral spacing affect hydrodynamic forces and moments on two ships (KVLCC2 and Aframax) during non-parallel berthing. Eighteen Kriging models were developed and tested across three parameter groups, with performance varying by response type—CaseA showed the highest accuracy (R² ~0.96–1.00), while CaseH and CaseY had mixed results, including some negative R² values. The findings highlight that Kriging model reliability depends on specific conditions and responses, limiting their use to validated scenarios. Water depth effects were analyzed within moderate-to-large depth-to-draft ratios, not shallow-water conditions.
This paper introduces a trajectory planning method for autonomous surface vehicles that doesn’t rely on pre-set guide paths. It combines model predictive control (MPC) with a turning circle-based control barrier function (TC-CBF), which considers the vehicle’s turning ability and nonholonomic motion. The approach creates distinct left- and right-turning avoidance modes, improving success rates and reducing safety violations in dynamic environments. Simulations show it outperforms single-mode methods in traffic-heavy scenarios.
This paper presents an adaptive neural-network-based control method for unmanned surface vessels during automatic berthing under cyber attacks. It addresses unknown vessel dynamics, external disturbances, measurement noise, and cyber attacks using a unified nonlinear measurement model to handle signal distortions. The approach reconstructs vessel heading from unaffected yaw-rate data, employs neural networks to approximate dynamics, and uses adaptive laws to estimate uncertainties. Simulations demonstrate the method's effectiveness in maintaining bounded signals and reducing berthing tracking errors despite time-varying attacks and disturbances.
This paper introduces a control framework (DO-ANFSMC) for collaborative autonomous surface (ASV) and underwater (URV) vehicles in search-and-rescue missions, addressing environmental disturbances and obstacle avoidance. The method combines adaptive neuro-fuzzy systems, disturbance observers, and sliding-mode control, with parameters optimized via an improved grey wolf optimizer (IGWO-CT). Simulations with one ASV and two URVs demonstrated low yaw-rate tracking errors (0.00185–0.01915 rad/s) and reduced path lengths (68.171–79.399 m) while ensuring obstacle clearance. Results show improved trajectory tracking, disturbance rejection, and navigation efficiency compared to alternative approaches.
This paper addresses fault-tolerant control for underactuated unmanned surface vessels (USVs) with rotatable thrusters experiencing thruster faults. It introduces a dynamic compensation controller to manage input constraints and allocation errors, improving accuracy and convergence speed. A multi-channel event-triggered mechanism reduces unnecessary updates, saving network bandwidth, while a finite-time disturbance observer estimates faults and external disturbances. Theoretical analysis and simulations confirm the method’s stability and efficiency in fault compensation and communication.
364crossref2026-05-27Reno Aditya Hendriantoro, Capt. Firdaus Sitepu, Dian Junita Arisusanty, I'ie SuwondoDOI ↗ (opens in a new tab) This study examines ship management in Indonesia’s narrow channels, focusing on the Amamapare Channel, where accidents rise due to larger ships, logistics demands, and hydrodynamic issues like bank effect and squat. Researchers analyzed the MV Meratus Dili’s voyages, interviewing officers and observing operations, finding speeds above 6 knots at low tide reduce Under Keel Clearance (UKC) by 0.35 meters. The optimal speed for safety is 4–5 knots during low tide or high traffic, maintaining a UKC of at least 0.5 meters. Improved standard operating procedures (SOPs), including checklists and training, could reduce accidents by 20–30%.
365maritimeprofessional.com2026-05-08
ABS and HD Korea Shipbuilding & Offshore Engineering (HD KSOE) are collaborating to advance digital shipbuilding and vessel intelligence. The partnership aims to integrate digital technologies into ship design, construction, and operation. Their focus includes improving efficiency, safety, and performance through data-driven solutions. The initiative reflects industry efforts to modernize maritime operations.
This study introduces a combined task-path optimization method for unmanned surface vehicle (USV) groups to improve efficiency by integrating task allocation and path planning. The approach uses a hierarchical framework with real-world ocean current disturbances and algorithms like an enhanced Hungarian method and SCA-APF to avoid local minima. Simulations show an 11.1% cost reduction and 80.5% better task allocation efficiency compared to genetic algorithms, with over 99% task completion in dynamic settings. The system remains fast, processing each step in under 0.1 seconds for swarms of up to 32 USVs.
This paper introduces a "backseat driver" system for an autonomous underwater glider to passively track sperm whales using their echolocation clicks. The system, mounted on a modified SeaExplorer glider, uses a four-hydrophone array to detect, separate, and estimate the angle of whale sounds, adjusting the glider’s direction in real time. Field trials demonstrated the system’s ability to follow whales at distances over 100 meters with minimal noise impact. The design enables long-term passive acoustic monitoring, with open-source code and data available.
This study presents the design and analysis of a 7-meter V-type trimaran hull for an Unmanned Surface Vehicle (USV) using SolidWorks and ANSYS. The research combines computational fluid dynamics (CFD) and structural analysis to assess hydrodynamic efficiency, stability, and resistance to external forces, including stress and deformation. Modal analysis was also conducted to evaluate the hull’s response to environmental vibrations. The integrated approach aims to optimize USV performance for high-speed and rough sea conditions while supporting defense industry applications.
369ASDNews2026-10-22
Blue Ops launched its B-Series maritime portfolio, featuring two production-ready uncrewed surface vessels: the B-18 and B-39. The announcement, made on October 22, 2026, highlights the company’s expansion into autonomous maritime technology. No additional technical specifications or operational details were provided in the release. The vessels are positioned as ready for deployment.
370Naval News2026-10-19
HD Hyundai and Anduril successfully demonstrated the integration of VAone and Lattice unmanned surface vessel (USV) autonomy systems in October 2026. The collaboration showcased combined capabilities for autonomous maritime operations. The test highlighted interoperability between the two platforms. No further technical or operational details were provided.
This study introduces a nonlinear control method (SPT-TACC) for unmanned surface vessels (USVs) to achieve synchronized, disturbance-resistant target tracking within a predefined time. The approach converts error models into periodic delayed forms and designs a feedback-based system ensuring simultaneous arrival at tracking points. Stability is rigorously proven, and guidelines for setting convergence times are provided to prevent system divergence. Simulations confirm the method’s effectiveness.
372TradingView2026-10-14
EXA reported a 21% organic growth in Q3 2026, driven primarily by strong performance in maritime robotics and navigation sectors. The company’s expansion in these areas contributed significantly to its overall quarterly results. No additional financial or operational details were provided.
SeaReTrack is a multi-object tracking framework designed for unmanned surface vehicles (USVs) to improve maritime perception. It addresses challenges like wave-induced camera motion, unstable detections, and target re-entry by combining motion-compensated association, HSV-based appearance verification, and boundary-aware identity recovery. Tested on the USVTrack benchmark, it achieved 64.35 HOTA and 80.09 IDF1 with 28 identity switches while maintaining real-time processing. The system also performed well in adverse conditions like rain, low light, and target reappearances.
374safety4sea2026-10-07
AI, automation, and connectivity may shape the future of the shipping industry. These technologies could enhance efficiency, safety, and operational capabilities. The article suggests they will play a key role in advancing maritime logistics. No specific methods or outcomes are detailed in the provided text.
This study evaluates the vessel passing capacity of the Changshan Waterway, a critical route between the Bohai and Yellow Seas. Using AIS data and a simulation model in AnyLogic, researchers estimate the waterway’s annual throughput capacity at 248,000 vessels. Current usage is at 66.4% saturation, nearing operational limits. The paper suggests speed regulation and spacing adjustments to improve capacity.
376Marine Link2026-07-26
HD Korea Shipbuilding & Offshore Engineering (KSOE) and Siemens Digital Industries Software partnered to create an AI-powered shipyard. The collaboration focuses on integrating the entire shipbuilding process, covering vessel design, production, and supply chain management. The initiative aims to develop a next-generation marine platform. No specific implementation timeline or technical details were provided.
This paper introduces "Social Mini-Games" (SMGs) as constrained, high-agency environments (e.g., doorways, hallways) where robots must navigate while interacting with humans and other robots. It highlights inconsistencies in assumptions, objectives, and evaluation methods across existing SMG navigation research, creating challenges for comparisons and practical applications. The authors propose a unified taxonomy and definitions to standardize the field, classify current methods, and outline evaluation protocols. The work aims to guide future research and lower barriers for new researchers in multi-robot navigation.
This paper introduces a hybrid deep reinforcement learning (HDRL) framework to manage task-heterogeneous formation control for unmanned surface vehicles (USVs). The approach splits tasks into leader path planning (using single-agent DRL) and follower formation tracking (using multi-agent DRL), incorporating real USV physical constraints. A dynamic gating mechanism and mirror mapping improve control performance and policy sharing. Simulations show a 44.59% increase in planning success rate over single-agent DRL and a 21.79–39.64% improvement in tracking performance over multi-agent DRL.
379Unmanned Systems Technology2026-10-29
A partnership aims to introduce autonomy-ready electric propulsion systems in France. The collaboration focuses on advancing unmanned vehicle technology. The initiative targets improved efficiency and sustainability in propulsion solutions. Details on specific companies or technologies involved are not provided.
This study introduces a two-stage neural network framework for controlling the swimming direction of an underactuated underwater walking robot with electromagnetic feet and propellers. The first network maps target directions to foot-tip positions using a mixture density model, while the second converts these positions into joint angles. Validation in simulation and in-air experiments showed mean direction tracking errors of 4.25° and 1.53°, respectively. The approach addresses challenges in non-unique inverse kinematics for the robot’s four-degree-of-freedom legs.
This paper introduces a mathematical model for optimizing routes in a mixed fleet of autonomous and human-driven freight vehicles. It aims to minimize emissions, energy use, and costs while maximizing reliability for autonomous vehicles, using a mixed-integer programming approach. The model includes a reliability threshold for autonomous vehicle paths and integrates into a decision support system for real-time adjustments. Valid inequalities, heuristics, and metaheuristics improve scalability, with a case study demonstrating its practical effectiveness.
This paper introduces an **ℒ₁-model predictive control (MPC) framework** using a *hand position* reference point for underactuated surface vehicles, improving trajectory tracking under disturbances and model uncertainties. By redefining the control point ahead of the center of gravity (CoG), sway disturbances—unmatched in CoG-based dynamics—become matched uncertainties, allowing effective adaptive compensation. The approach combines MPC for constraint handling with **ℒ₁ adaptive control** for real-time disturbance rejection. Simulations and field tests show better performance compared to baseline methods.
383Fortune Business Insights2026-09-21
The AI-Based Vessel Traffic Management market is projected to grow significantly by 2034. The report by Fortune Business Insights analyzes market size, share, and trends. It focuses on advancements in AI technology improving maritime safety and efficiency. No specific data or growth figures are provided in the given text.
This paper introduces an information-driven, multilevel path planning framework for unmanned surface vehicles (USVs) to improve environmental monitoring of uninhabited islands and reefs. The framework uses three algorithms: one for broad mapping of irregular areas, another for fine-tuned local mapping adjusted to tidal changes, and a third for energy-efficient ecological sampling despite ocean currents. Tests show the method outperforms existing approaches in path length, coverage accuracy, and energy use. The system enhances observation efficiency and reliability for long-term monitoring tasks.
This study introduces a wind-constrained framework for vessel traffic organization in port approach channels, using a Pareto-based multi-objective reinforcement learning algorithm (Pareto MO-PPO). The method incorporates real-time wind data to model propulsion, CO₂ emissions, and engine-load limits, enabling dynamic speed adjustments. Tested at Caofeidian Port, the approach reduced emissions by 11.41% compared to first-come, first-served scheduling, with a 7.49% increase in system time. The framework offers port operators multiple policy options to balance efficiency and emissions.
386openalex2026-09-21Sergey Vladimirovich Ermakov, Nikita Zhanovich Malinin, Danila Vladimirovich ShevchukDOI ↗ (opens in a new tab) This paper explores the development of digital twins for vessel operational processes like maneuvering and squat, highlighting limitations in current mathematical models and empirical formulas. It proposes digital twins as an alternative, outlining principles for their creation, including defining processes, their parameters, and structural components. The authors distinguish between mathematical and digital models, analyze the "digital twin" concept, and present a framework for process-centric digital twins. Future research aims to establish a comprehensive methodology for their formation and application.
This study proposes an integrated rescheduling model for vessel sequencing and berth allocation to address disruptions caused by vessel delays at ports with one-way channels. It introduces a rolling decision-making mechanism and strategies like berth reallocation, Normal Berth Shifting (NBS), Cross-Terminal Berth Shifting (CTBS), and Cargo Discharge-Only (CDO) to minimize rescheduling costs. An integer linear programming model and an adaptive large neighborhood search (ALNS) algorithm optimize vessel sequencing and berth use. Case studies and sensitivity analysis confirm the framework’s effectiveness in reducing delay impacts.
This study evaluates the effectiveness of different search patterns (Random Walk, Spiral, Lawnmower, and Cluster Search) for Unmanned Surface Vehicles (USVs) in multi-target tracking using the Adaptive Particle Swarm Optimization with k-Nearest Neighbors (APSO–kNN) algorithm. Simulations show that systematic patterns like Spiral and Lawnmower improve coverage and accuracy, while Random Walk is less reliable due to its unpredictability. Cluster Search depends on target distribution, and a mixed strategy offers balanced performance. APSO–kNN enhances adaptability for real-world applications like surveillance and search-and-rescue missions.
This paper introduces an adaptive fault-tolerant control method for underactuated unmanned surface vehicles (USVs) to maintain trajectory tracking despite actuator faults and external disturbances. The approach combines prescribed performance control, radial basis function neural networks, and barrier Lyapunov functions to keep tracking errors within set limits while addressing both multiplicative and additive faults. A smooth shifting function allows the controller to handle initial constraint violations, improving operational flexibility. Simulations show the controller achieves position errors below 0.04 m and yaw errors below 0.03 rad, even with 50% actuator degradation and multiple fault events.
This paper introduces a frequency-aware formation control method for unmanned surface vehicle (USV) clusters to address challenges like environmental disturbances, noisy measurements, and actuator limitations. The approach combines command-filtered backstepping, sliding-mode control, a saturation-protected high-gain observer, and momentum-based disturbance estimation to mitigate phase lag, observer peaking, and communication issues. The framework ensures bounded tracking errors and stability through Lyapunov-based analysis. Simulations confirm the method's effectiveness and improved performance.
391crossref2026-05-13Filiberto Muñoz Palacios, Eduardo S. Espinoza, Jorge Said Cervantes-Rojas, Jesus Patricio Ordaz Oliver +2 moreDOI ↗ (opens in a new tab) This paper presents a control strategy for unmanned surface vehicles (USVs) to track trajectories despite unknown hydrodynamic parameters and underactuation. A sliding-mode dynamic neural network identifier estimates the USV’s dynamics, while a nonsingular sliding-mode controller ensures stability. Lyapunov analysis confirms finite-time convergence of tracking and identification errors, with simulations validating performance under actuator limits and disturbances. The approach matches state-of-the-art controllers without requiring prior knowledge of system parameters.
This paper presents a fault-tolerant control (FTC) algorithm for unmanned surface vehicles (USVs) to maintain formation under challenges like disturbances, model uncertainties, actuator faults, and input saturation. The method combines cooperative learning neural networks, distributed disturbance observers, and backstepping techniques, with shared neural network weights improving coordination. An auxiliary system and actuator matrix address input saturation and propeller failures, ensuring errors stay within predefined performance limits. Simulations with four USVs confirm the algorithm’s effectiveness in maintaining formation and reducing tracking error oscillations compared to non-learning methods.
393Ocean News & Technology2026-11-05
Naval Group and Thales launched CortAIx France on November 5, 2026, to enhance artificial intelligence capabilities for naval applications. The initiative aims to develop advanced AI solutions tailored for defense and maritime security. CortAIx France will focus on improving decision-making, autonomy, and operational efficiency in naval systems. The collaboration combines expertise from both companies to strengthen France’s position in naval AI innovation.
394헤럴드경제2026-10-21
The Korea Research Institute of Ships and Ocean Engineering (KRISO) showcased advanced ocean technologies, including AI-powered ships and underwater robots. These innovations aim to enhance maritime operations and exploration. The developments were presented as part of KRISO’s vision for future ocean technology. No specific technical details or results were provided in the announcement.
395Business Wire2026-10-15
Fincantieri is speeding up the use of robotics and physical AI to modernize its shipyards. The company aims to create the "shipyard of the future" by integrating advanced technologies. This initiative focuses on improving efficiency and innovation in shipbuilding. No specific methods or outcomes are detailed in the provided text.
396Onalert2026-10-05
Hivemind AI software enables uncrewed surface vessels (USVs) like the British K3 SCOUT to operate as an autonomous team. The system was demonstrated to coordinate multiple USVs without human intervention. This technology aims to enhance collaborative missions for naval or maritime applications. The K3 SCOUT USVs were used to showcase the software’s capabilities.
397Siemens Newsroom2026-10-01
Siemens and HD Hyundai will collaborate to create an AI-powered digital shipyard aimed at modernizing U.S. shipbuilding. The initiative focuses on integrating advanced digital technologies to enhance efficiency and productivity. The partnership seeks to transform traditional shipbuilding processes through automation and data-driven solutions. No specific timeline or technical details were provided in the announcement.
This paper discusses the role of unmanned marine vehicles (UMVs), such as surface vehicles, underwater vehicles, and gliders, in ocean exploration, resource development, and environmental monitoring. It covers key technical aspects, including perception, planning, control, and swarm coordination. UMVs are highlighted as essential tools for maritime security and search-and-rescue operations. The work provides an overview of current advancements and applications in the field.
This study examines how the bank effect—hydrodynamic forces between a vessel and fairway banks—influences navigation safety for full-form vessels like tankers. Using a validated navigational simulator, researchers tested vessel trajectories near sloped banks at varying speeds and distances, comparing results with towing tank data. A polynomial regression model (accuracy: 0.9746) determined minimum safe distances, revealing sloped banks can pose greater grounding risks than vertical walls, especially when vessels sail over the slope. The findings highlight the need for situation-specific safety measures to reduce navigation risks.
This study presents a safety control method for underactuated unmanned surface vehicles (USVs) navigating routes with moving obstacles. It integrates velocity-obstacle techniques with COLREGs (collision avoidance regulations) and a fixed-time fuzzy observer to ensure compliance and real-time adaptability. The approach was validated through both theoretical analysis and experimental testing. Results demonstrate improved collision avoidance while maintaining waypoint-following accuracy.
The second edition of *Maritime security and risk assessments* addresses evolving challenges in maritime safety, including digitization, automation, cyber-physical risks, and geopolitical threats. It expands on the first edition by exploring risk-informed decision-making, validation, and system resilience through three research streams: collision-risk sensing, human-technology interaction, and port safety. Studies use traffic data, simulations, and accident reports but face limitations like incomplete validation, sparse data, and weak cross-scale integration. Effective implementation requires trustworthy data, uncertainty management, human-centred assurance, and preparedness for cyber-physical and security failures.
This paper addresses optimal trajectory tracking for under-actuated Maritime Autonomous Surface Ships (MASS) with unknown dynamics using a dynamic event-triggered (DET) data-driven (DD) control approach. It employs identifier neural networks to estimate unknown ship dynamics from navigation data. The method aims to improve efficiency by triggering control updates only when necessary. The study proposes a solution for real-time tracking control in MASS systems.
The paper examines the shift in the automotive industry toward AI-defined vehicles (AIDVs), driven by AI integration, advanced data processing, and sensor-communication convergence. It explores the progression from connected vehicles to software-defined vehicles (SDVs) and ultimately AIDVs, which use generative AI for scenario synthesis and agentic AI for decision-making. The authors analyze the transition from hardware-centric to AI-centric designs, emphasizing the role of vehicle-to-everything (V2X) communication. The article also discusses future challenges and opportunities in automated intelligent transportation systems (ITSs).
404openalex2026-08-03Elizabeth Dietrich, Hanna Krasowski, Emir Cem Gezer, Roger Skjetne +2 more
This paper introduces a real-time monitoring framework for robotic systems operating under uncertainty, reducing data needs by using data-driven reachable sets to evaluate task and safety specifications. The approach is applied to maritime navigation, addressing complex traffic rule requirements with a data-efficient method for constructing reachable sets. The framework is validated through simulations and hardware tests, showing improved risk detection over existing metrics under realistic conditions. The work focuses on real-time deployment and robustness to disturbances.
405openalex2026-07-21Yuan Gui, Hongchen Luo, Liqi Qu, Longyue Fu +1 more
The paper introduces **NaviAIS**, a standardized dataset for vessel trajectory prediction, addressing limitations in existing AIS datasets like inconsistent sampling, noise, and lack of structured navigational data. It provides unified temporal windows, local coordinate systems, vectorized lane priors, and rasterized navigable maps to improve reproducibility and environment-aware forecasting. The authors also propose **NaviLane**, a hierarchical framework that encodes trajectory-map interactions, generates multimodal predictions, and refines them using a consequence-aware evaluator. Experiments demonstrate NaviLane’s superior performance over baseline methods in both single-modal and multimodal prediction tasks.
406openalex2026-07-02Agnes N. Mwange, Taichi Kambara, Kouki Wakita, Kazuyoshi Hosogaya +1 more
This study introduces a simplified linear state-space model to represent low-speed ship dynamics, addressing challenges in autonomous berthing and unberthing. The model's parameters were estimated using the Covariance Adaptation Strategy Evolution Strategy (CMA-ES) applied to full-scale maneuvering data. Validation showed the model closely matched empirical data. The findings suggest linear models can effectively capture ship maneuvering at low speeds.
This paper explores adaptive navigation strategies for Autonomous Underwater Vehicles (AUVs) used in ocean exploration, monitoring, and military tasks. It highlights challenges like unpredictable ocean currents, moving obstacles, sensor limitations, and communication issues in dynamic marine settings. The study aims to improve AUV reliability in changing underwater conditions. No specific methods or results are detailed in the provided text.
408openalex2026-06-03Xueming Liu, Lin Li, Xiang Zhou, Tianjiang Hu +1 more
This paper presents a method for multiple unmanned surface vehicles (USVs) to circle a target without relying on external positioning systems. The USVs use onboard sensors and communication to measure distances and angles, employing two types of Kalman filters to estimate positions relative to the target and each other. A formation controller based on coupled oscillators ensures the system remains observable while maintaining a uniform circular formation. Simulations confirm the approach works effectively.
409openalex2026-06-02Stepan Konev
The paper introduces StandardE2E, a unified framework for end-to-end autonomous driving datasets. It standardizes preprocessing, enabling cross-dataset experimentation and combined training via a single PyTorch DataLoader. The framework simplifies adding new datasets by requiring only a mapping to a shared schema. It currently supports six datasets and is available as an open-source Python package.
This paper introduces an interpretable reinforcement learning framework for autonomous warehouse robots, combining deep reinforcement learning with explainability methods like saliency mapping and feature importance analysis. The approach improves transparency and trust by making the robot’s decision-making process understandable to operators. Experimental results showed a 98% collision rate in one outcome, while successful runs achieved faster navigation with an average time of 31.87 seconds and a path smoothness score of 0.539. The framework enhances efficiency and reliability for logistics and inventory management applications.
411openalex2026-05-26Yuhang Zhang, Shuqi Chai, Yukang Zhang, Liusha Yang +4 more
This paper introduces a reinforcement learning framework for safe USV navigation that accounts for perception uncertainty and compliance with maritime collision regulations (COLREGs). The method combines credibility-weighted learning to adjust for noisy sensor data, a geometric safety shield to prevent hazardous actions, and continuous rule-aware signals to smooth navigation decisions. The approach improves training stability and reduces collisions compared to existing methods. Simulations show better performance in dynamic environments with unreliable perception systems.
The paper by Zeng and Xie (2026) addresses inefficiencies in deep reinforcement learning (DRL) for mapless navigation caused by uniform experience sampling. Their Goal-guided Greedy Experience Replay (GER-RL) method prioritizes important experiences using non-uniform sampling to improve data utilization. Simulation experiments show GER-RL enhances navigation performance, increasing average reward, success rate, and reducing collision rate, time, and path length. The approach outperforms existing DRL-based methods in key metrics.
This paper by A. Dhyani (2026) presents a framework for safe autonomous vessel navigation in inland waterways under uncertain and abnormal conditions. It introduces a robust system identification method for vessel maneuvering models, a Nonlinear Model Predictive Control (NMPC) system for constraint-compliant navigation, and a sensor Fault Detection and Isolation (FDI) scheme to monitor and diagnose sensor faults. The study also proposes a risk mitigation approach using Bayesian Belief Networks and Partially Observable Markov Decision Processes to handle failures like collisions or grounding. Validation includes full-scale trials, simulations, and case studies demonstrating improved prediction accuracy, control performance, and fault resilience.
This paper introduces a multi-stage training framework using transfer learning to improve Deep Reinforcement Learning (DRL) for autonomous surface vessel (ASV) path following. The approach first trains a DRL controller on simplified vessel dynamics (Nomoto models) before transferring the learned policy to a complex nonlinear MMG model, reducing training time. The controller is fine-tuned with environmental disturbances and evaluated using heading and cross-track errors. Performance is compared against a traditional PID controller to assess effectiveness.
A deep reinforcement learning (DRL) controller using Proximal Policy Optimization (PPO) was trained and tested on a full-scale autonomous ferry prototype. The model, trained in a ROS-based 3-DOF simulator for 35 hours, was directly deployed on the physical vessel *milliAmpere1*. Field trials in Trondheim demonstrated the controller met regulatory dynamic positioning (DP) requirements, maintaining station within 4 meters and achieving ±0.25 m and ±5° accuracy after initial adjustments. Minor oscillations were attributed to velocity estimation and environmental factors.
416openalex2026-04-03大月 智史, S. Otsuki, 八田 直樹, N. Hatta +7 more
This paper presents a hierarchical framework for ensuring safe vessel operations in ports using Control Barrier Functions (CBF), Model Predictive Control (MPC), and a Rapidly-exploring Random Tree (RRT)-like spatiotemporal path planning method. The approach integrates real-time collision avoidance and optimal trajectory planning to enhance navigation safety. The authors demonstrate the system's effectiveness through simulations and experiments in port environments. The proposed method aims to reduce risks and improve operational efficiency for autonomous or semi-autonomous vessels.