(03)

Science

↓ (W22 ‘26 · May 25–31)
01crossref2026-05-28А. А. Butsanets, А.П. Беспалов, A. M. ZhidkovaDOI ↗ (opens in a new tab)

Development of the component composition of the automatic control system for maritime autonomous surface ships under external disturbances (opens in a new tab)

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.

02crossref2026-05-28Павло Носов, Oleksiy Melnyk, Tomáš Kalina, Martin Jurkovič +4 moreDOI ↗ (opens in a new tab)

Fractal–Episodic Assessment of Ship Control Microvariability for Human-Factor-Aware Navigation Risk Monitoring in Maritime Autonomous Systems (opens in a new tab)

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.

03openalex2026-05-29Anthony Saaiby, Mayur S. Patil, Prabhakar R. Pagilla, Sivakumar Rathinam +1 moreDOI ↗ (opens in a new tab)

A Framework for Identification and Prioritization of Critical Factors in Transition Between Manual and Autonomous Navigation Functions in Marine Vessels (opens in a new tab)

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.

04crossref2026-05-29Lirong Kou, Xiaoyang GaoDOI ↗ (opens in a new tab)

Advances in Optimized and Safe Path Planning of Marine Autonomous Surface Vehicles: A Review (opens in a new tab)

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.

05openalex2026-05-29Sara Cabañas AreaDOI ↗ (opens in a new tab)

Between Limitation and Non-Limitation: Regulatory Overlap between the LLMC and Directive (EU) 2024/2853 in Autonomous Shipping (opens in a new tab)

The paper explores conflicts between the **LLMC** (Convention on Limitation of Liability for Maritime Claims) and **EU Directive 2024/2853**, which extends product liability to software in autonomous ships. Directive 2024/2853 imposes strict liability for defective products and prohibits limitation in such cases, potentially overriding the LLMC’s liability caps. Shipowners may be classified as "economic operators" under the Directive, exposing them to unlimited liability for navigation system failures. The analysis concludes that EU law’s primacy may restrict the LLMC’s application in product defect claims, reshaping maritime liability for autonomous vessels.

06openalex2026-05-26Josep Marin Garces, Andrea Pino Jarque, Salvador López Barajas, Guillem Fustier Castillo +6 moreDOI ↗ (opens in a new tab)

Experimental Deployment and Recovery of an Underwater Robot from an Autonomous Surface Catamaran Using an Integrated LARS and Immersive Augmented Reality Teleoperation Interface (opens in a new tab)

This study demonstrates a field test at Spain’s Port of Castellón, where a catamaran surface robot with an integrated Launch and Recovery System (LARS) deployed and retrieved a BlueROV underwater vehicle. The trial covered navigation, remote supervision, underwater operations, and recovery using an immersive augmented reality teleoperation interface. Conducted by CIRTESU and IRSLab, the experiment highlights advancements in cooperative surface–underwater robotics and autonomous maritime technologies. The work supports research on robotic inspection and intervention in port and marine environments.

07openalex2026-05-31L. Vagushchenko, A. Kozachenko, J. KozachenkoDOI ↗ (opens in a new tab)

Interpretation and updating of regulations for preventing collision at sea (opens in a new tab)

This paper examines updates needed for the International Regulations for Preventing Collisions at Sea (COLREGS) due to advancements like larger and faster ships, new navigation technologies, and autonomous vessels. It proposes methods to interpret COLREGS, including defining danger zones and determining timely actions for ships. The authors critique existing rules, suggest amendments, and emphasize classifying vessel approach situations. The study also addresses incorporating regulations for Maritime Autonomous Surface Ships (MASS) into COLREGS.

09openalex2026-05-28V. V. Astrein, S.I. Kondratiev, В.И. ФилатовDOI ↗ (opens in a new tab)

Situational awareness: from a data processing paradigm to a perception management paradigm (opens in a new tab)

This paper proposes a new architecture for situational awareness in Level 4 autonomous marine vessels, addressing limitations in traditional data fusion methods that fail to differentiate sensor failures, accuracy issues, or cyberattacks. The system includes a non-switchable core of essential sensors, a Sensor Trust Manager to isolate unreliable data sources, and a navigation scenario classifier that adjusts sensor confidence weights dynamically. The approach shifts from data fusion to "source trust management," introducing meta-awareness to monitor both the environment and sensor reliability. The design aims to comply with maritime safety regulations while improving unmanned navigation safety.

10crossref2026-05-30Yiqi Shi, Xiang Liu, Yueying Wang, Weidong ZhangDOI ↗ (opens in a new tab)

Co-Optimized Target Perception and Disturbance Estimation for Unmanned Surface Vessels (opens in a new tab)

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.

11crossref2026-05-19Tinotenda Chimbwanda, Tyler Bettencourt, Nathalie Risso, Tejo Bheemasetti +2 moreDOI ↗ (opens in a new tab)

Surface Mine Planning Adaptations for the Integration of Autonomous Haulage Systems: A Review (opens in a new tab)

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.

12openalex2026-05-28Lingyue Li, Xiaozhi Ma, Jing YangDOI ↗ (opens in a new tab)

A bibliometric analysis on Internet of Things research for maritime industry 4.0 (opens in a new tab)

This paper analyzes IoT research in the maritime Industry 4.0 sector using bibliometric data on publications, keywords, and citations. It identifies four key research areas: digital infrastructure, data analytics, navigation technologies, and smart operations. The study highlights five future directions, including AI integration, autonomous systems, and sustainable IoT solutions. Environmental sustainability and system resilience are noted as understudied topics.

14openalex2026-05-30Daniel Rosehill, Gemini 3.1 (Flash), Chatterbox TTSDOI ↗ (opens in a new tab)

What Submarines Actually Do Underwater (opens in a new tab)

Submarines serve multiple roles beyond nuclear deterrence, with only 30% of the global fleet dedicated to this purpose. Attack submarines conduct anti-surface and anti-submarine warfare, intelligence gathering, special operations support, and sea denial by creating uncertainty for adversaries. They also launch cruise missiles, as seen in U.S. and Russian strikes, and some are modified for undersea espionage, such as tapping fiber optic cables. Missions like these are often classified, with submarines like the USS Parche and USS Jimmy Carter playing key roles.

15crossref2026-05-27Reno Aditya Hendriantoro, Capt. Firdaus Sitepu, Dian Junita Arisusanty, I'ie SuwondoDOI ↗ (opens in a new tab)

Optimizing Ship Management In Narrow Shipping Channels To Reduce The Percentage of Ship Accidents (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%.

17openalex2026-05-29Liudmyla Cheroy, Maryna Moniushko, Olha BazhakDOI ↗ (opens in a new tab)

EDUCATION IN THE FIELD OF HYDROMETEOROLOGICAL AND HYDROGRAPHIC SUPPORT OF NAVIGATION: CONTEMPORARY TRANSFORMATIONS AND DEVELOPMENT PROSPECTS (opens in a new tab)

This paper examines modern approaches to training specialists in hydrometeorological and hydrographic support for navigation, emphasizing the need for updated education due to digitalization, stricter safety demands, and evolving maritime conditions. It highlights the shift toward an integrated, competence-based model combining hydrography, meteorology, oceanography, and geoinformation technologies, with a focus on digital tools, big data, and simulation training. The study compares Ukraine’s education system—strong in theory but needing more interdisciplinary and practical integration—with international standards. Key recommendations include adopting digital platforms, practice-oriented learning, and analytical skill development to improve decision-making in complex maritime environments.

18crossref2026-05-27Suhail Ashfaq Butt, Umer Farooq, Muhammad Shan, Muhammad Faisal +3 moreDOI ↗ (opens in a new tab)

Interpretable Reinforcement Learning Framework for Autonomous Warehouse Robot Navigation (opens in a new tab)

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.

19openalex2026-05-26Yuhang Zhang, Shuqi Chai, Yukang Zhang, Liusha Yang +4 more

Trust, Geometry, and Rules: A Credibility-Aware Reinforcement Learning Framework for Safe USV Navigation under Uncertainty (opens in a new tab)

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.

© 2026 Ship Autonomy · Summaries are AI-generated; articles belong to their publishers · Privacy · Contact