An E-APF-based approach for dynamic path planning and COLREGs-aware navigation of Maritime Autonomous Surface Ships (opens in a new tab)
No abstract or body text was available, so this entry is reported from metadata only.
Science
02crossref2026-06-25Afifah Nurrosyidah, Pandu Dwi Luhur Pambudi, Ashari Fitra RachmannullahDOI ↗ (opens in a new tab)
A Smart Port Readiness Index in Southeast Asia to support autonomous ship operations (opens in a new tab)
This study created a Smart Port Readiness Index (SPRI) to evaluate port preparedness for Maritime Autonomous Surface Ships (MASS) in Indonesia, Malaysia, Thailand, Singapore, and the Philippines. The index measured six dimensions, including technology, cybersecurity, and regulations, using policy text mining and a simulation framework. Singapore ranked highest in readiness, while Indonesia, Thailand, and the Philippines showed low capacity for MASS operations. The findings suggest prioritizing investments in autonomous technologies and cybersecurity to improve port readiness.
Real-time Collision Avoidance Considering Ship Position Uncertainty Based on AIS Data (opens in a new tab)
This study presents a collision avoidance framework for autonomous ships that accounts for positional uncertainty in AIS data. An unscented Kalman filter estimates the motion and uncertainty of nearby vessels in real time, improving trajectory predictions. Collision risk is assessed using a quantitative index, and avoidance maneuvers are generated via the velocity obstacle method. Simulations demonstrate a 96% reduction in estimation error, achieving ~3 m positional accuracy compared to nonpredictive approaches.
A Human-Centred Framework for Maritime Education and Training in Coastal Remote Autonomous Ship Operations (opens in a new tab)
No abstract or body text was available, so this entry is reported from metadata only.
A port navigation perception method via shipborne camera-guided light detection and ranging fusion (opens in a new tab)
This study introduces SLC-Fusion, a multimodal perception system for Maritime Autonomous Surface Ships (MASS) combining shipborne LiDAR, cameras, IMU, and RTK data. It improves sensor calibration, motion compensation, and LiDAR denoising while using AI (YOLO 11s-seg) and spatial clustering to detect and localize port targets in 3D. Real-world tests show high accuracy with a minimum ranging error of 0.07 m, a mean IoU of 67.0%, and fast processing times (5.9 ms for images, 4.6 ms for point clouds). The method also establishes a port navigation perception dataset.
A Machine Learning Operations Framework for Self-Adaptive Anomaly Detection in Autonomous Surface Ships Under Data Drift (opens in a new tab)
This paper introduces a machine learning operations (MLOps) framework for real-time anomaly detection in autonomous surface ships, focusing on engine conditions. The system uses a hybrid model combining an attention LSTM autoencoder and isolation forest to detect subtle anomalies across engine components. To handle data drift, an onboard MLOps pipeline monitors sensor data shifts and autonomously retrains models when performance drops. Tests on a fault simulation testbed show the system maintains detection reliability under changing conditions.
<b>Real-Time Steering Guidance Model for MASS Remote Operators Using Few-shot Analogous Episodes</b> (opens in a new tab)
This paper introduces a real-time steering guidance model for remote operators managing multiple maritime autonomous surface ships (MASS). It focuses on retrieving similar past operational episodes based on current ship conditions and training a few-shot dataset. The approach incorporates Human-Computer Interaction (HCI) principles to provide effective steering recommendations. The goal is to assist operators in handling multiple vessels simultaneously.
The Labrador Coastal Current: Observations from Surface Drifters and Autonomous Gliders (opens in a new tab)
This study examines the Labrador Coastal Current (LCC) using data from surface drifters (Global Drifter Program and CONOC project) and autonomous gliders. The LCC, near the Labrador coast, has moderate speeds up to 0.25 m/s, while the main Labrador Current (MLC) over the shelf break reaches 0.5 m/s. Surface velocities in the LCC are mostly barotropic (70%), whereas the MLC is mainly baroclinic (70%). Despite lower volume transport, the LCC’s freshwater transport is comparable to the MLC.
Towards fully autonomous ship hull inspection for security relevant attached objects using AUVs equipped with imaging sensors (opens in a new tab)
No abstract or body text was available, so this entry is reported from metadata only.
GOVERNED AUTONOMOUS INFRASTRUCTURE (GAI) The AETHELIS Ecosystem: Operational Architecture for Governed Autonomous Intelligence. (opens in a new tab)
The paper introduces AETHELIS, an operational ecosystem for Governed Autonomous Infrastructure (GAI), addressing the shift from model-centric AI to infrastructure-driven autonomous systems. It integrates governance, adaptation, simulation, and orchestration into four layers: Sentinel (runtime control), ARL (self-improvement), Synapsis (simulation-based intelligence), and AEGIS (execution orchestration). The work proposes concepts like Governance Drift and the Constitutional Substrate to ensure long-term stability, accountability, and verifiable governance. AETHELIS is presented as a deployable architecture for governing autonomous intelligence and its evolution.
Simulation-Based Maritime Scheduling Optimization for Bidirectional Ship Flow in Multi-Chamber Lock Systems: Incorporating Chamber Operations for Efficient Management (opens in a new tab)
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.
Bradycardia inhibits brain vessel mural cell differentiation via reducing mechanosensory and Jag2-Notch signaling (opens in a new tab)
Bradycardia, induced chemically or genetically by targeting the Hcn4 heart pacemaker channel, reduces brain vessel diameter and mural cell numbers in embryonic development. The study finds decreased mechanosensitive signaling (Piezo1, Mek5, Erk5, Klf2) and lower expression of the Notch ligand jagged2b in bradycardia models. Knockdown of jag2b reduces mural cells, while restoring its levels increases mural cell numbers in both wildtype and hcn4 mutants. This links bradycardia to impaired mural cell recruitment via disrupted Notch signaling.
Passive Acoustic Monitoring of Leaks in Underwater Wells and Pipelines (opens in a new tab)
Passive Acoustic Monitoring (PAM) detects underwater leaks in wells and pipelines by capturing natural acoustic signals from escaping fluids, bubbles, and vibrations using hydrophones and distributed sensing networks. Signal processing and machine learning help distinguish leak signatures from background noise, improving reliability in real-time monitoring. While effective, challenges like false positives, sensor placement, and performance in noisy environments persist. Research focuses on enhancing detection sensitivity and accuracy through multi-modal sensing and AI-driven localization.
14crossref2026-12-31K. Arul Deepa, M. Preethivee, M. S. Karthika Devi, P. RamyaDOI ↗ (opens in a new tab)
Enhancing autonomous navigation systems through reinforcement learning (opens in a new tab)
No abstract or body text was available, so this entry is reported from metadata only.
15openalex2026-06-25Myounghoon Kim, Seongkyeong Jeong, Jinhyun Park, Sunggil Kim +1 moreDOI ↗ (opens in a new tab)
Formulation of Integrated Heavy-class Remotely Operated Vehicle–Manned Diving Procedures for Underwater Operations in Large-scale Marine Disasters (opens in a new tab)
This study presents a combined procedure using heavy-class ROVs and manned diving to enhance safety and efficiency in underwater search and rescue during large marine disasters. It analyzes tidal-current impacts and operational limits, finding ROV-SSDS integration allows longer diving windows than ROV-SCUBA due to SCUBA’s stricter current restrictions. The procedure incorporates ROV-mounted sensors for situational awareness, target localization, and safety monitoring. A classification framework was also developed to guide the selection of ROV-only, manned-diving-only, or integrated operations based on disaster severity, vessel needs, and depth.
Dual-Channel Event-Triggered Prescribed Performance Control for USV Under False Data Injection Attack (opens in a new tab)
This paper proposes an adaptive neural network-based control method for unmanned surface vessels (USVs) to track trajectories despite false data injection (FDI) attacks, system uncertainties, and disturbances. It introduces a dual-channel dynamic event-triggered mechanism (DDETM) to reduce communication overhead and actuator wear while enabling thrust allocation. The approach uses a second-order prescribed performance function (SOPPF) and dynamic surface control (DSC) to maintain tracking accuracy and simplify computations. Simulations confirm the method’s effectiveness in ensuring stable and bounded system performance.
Peer Review Report For: Citizens marine pilots: Increasing autonomous surface vehicles navigation capabilities with and for the public [version 1; peer review: 1 approved, 1 approved with reservations] (opens in a new tab)
No abstract or body text was available, so this entry is reported from metadata only.
18openalex2026-06-22Jingyi Yao, Ning Huang, Wenjie Shi, Xinming Han +1 moreDOI ↗ (opens in a new tab)
DGP-SFNet: An efficient visual perception architecture for small maritime objects on unmanned surface vehicles (opens in a new tab)
No abstract or body text was available, so this entry is reported from metadata only.
Unmanned Marine Vehicles: Perception, Planning, Control and Swarm (opens in a new tab)
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.
Adaptive Navigation Strategies for Autonomous Underwater Vehicles in Time-Varying Marine Environments (opens in a new tab)
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.
The Future of Autonomous Transportation: Opportunities and Challenges in Nigeria (opens in a new tab)
The paper explores the potential of autonomous vehicles (AVs) to address road safety, congestion, and mobility issues in Nigeria. It highlights challenges such as weak infrastructure, regulatory gaps, and the need for context-specific analysis. The study emphasizes aligning AV adoption with Nigeria’s institutional, technological, and socio-cultural conditions. Limited existing evidence on AVs in African contexts is noted as a key concern.