(03)

Science

↓ (W36 ‘26 · Aug 31–Sep 6)
01crossref2026-08-31Ke Zhang, Jie Wen, Xingya Zhao, Jianan Luo +1 moreDOI ↗ (opens in a new tab)

A ship autonomous navigation decision-making method based on the OODA loop theory (opens in a new tab)

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.

02crossref2026-09-01J.H. Park, Chybyung Park, Han-Seon ParkDOI ↗ (opens in a new tab)

The role of Marine Aids to Navigation in Maritime Autonomous Surface Ships and the economic implications of remote operator training: A Korean perspective (opens in a new tab)

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.

03crossref2026-09-06Christophe Mundutéguy, David GuerreroDOI ↗ (opens in a new tab)

The development of maritime autonomous surface ships (MASS) from seafarers’ perspective: operational, spatial, and labour implications (opens in a new tab)

The paper examines maritime autonomous surface ships (MASS) from the perspective of seafarers, focusing on operational, spatial, and labour impacts. Interviews with maritime professionals reveal tensions between automation and manual tasks, challenges in complex coastal areas, and mixed views on the future of maritime jobs. Findings suggest MASS will redefine rather than eliminate human labour, requiring regulatory, training, and infrastructure adjustments. The study advocates for a human-centric transition framework that accounts for spatial differences and occupational expertise.

04openalex2026-09-01Kebin Cao, Sen Xu, Zhihao Zhu, Xinyu Shi +5 moreDOI ↗ (opens in a new tab)

Visual perception for autonomous surface vehicles in complex waterway environments: A review (opens in a new tab)

This review examines visual perception for autonomous surface vehicles in challenging waterway environments, highlighting dynamic disturbances, poor lighting, bad weather, and small targets as key obstacles. It organizes real-world waterway datasets by diversity and complexity while evaluating perception methods for robustness, dataset fit, and deployment limits. The paper analyzes how environmental factors cause perception failures and links model design, data quality, and practical constraints. Emerging solutions like multimodal perception and domain generalization are proposed to improve reliability in maritime applications.

05openalex2026-08-31Denny Darlis, Nurul Fahmi Adani Syam, Aris HartamanDOI ↗ (opens in a new tab)

Development of a Solar Autonomous Boat Navigation Guidance System for Future Swarm Unmanned-Surface-Vehicle Implementation (opens in a new tab)

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.

06openalex2026-09-01Chenguang Liu, Jialun Liu, Xiumin Chu, Xiao LangDOI ↗ (opens in a new tab)

Artificial Intelligence and Its Applications in Intelligent Ship Navigation (opens in a new tab)

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.

07crossref2026-09-01Chanaka Thushitha Bandara, Herbert G. TannerDOI ↗ (opens in a new tab)

Field-Validated Formation Control for Autonomous Surface Vehicle Fleets in Aquatic Environments with Currents (opens in a new tab)

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.

08crossref2026-09-01Kukatlapalli Pradeep Kumar, Peter J. Stavroulakis, Michael Moses Thiruthuvanathan, Periklis Prousaloglou +1 moreDOI ↗ (opens in a new tab)

Intelligent maritime vessel detection and classification toward offshore wind energy zone surveillance using YOLOv11 and explainable hybrid machine learning for autonomous maritime monitoring (opens in a new tab)

No abstract or body text was available, so this entry is reported from metadata only.

09openalex2026-08-31Shuang Gao, Xue Liu, Qiusu Wang, Yigeng Li +2 moreDOI ↗ (opens in a new tab)

Safe formation and path planning method for unmanned surface vessels in restricted navigation environments (opens in a new tab)

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.

11crossref2026-09-03Nikolay N. Ilyin, Nikita M. BotalovDOI ↗ (opens in a new tab)

Forensic Examination of Unmanned Surface Vessels: Obtaining Forensically Significant Information (opens in a new tab)

The paper explores how unmanned surface vessels (USVs) can serve as forensic evidence in criminal cases due to their autonomous operation, remote control capabilities, and capacity to carry explosives discreetly. It highlights the unique challenges posed by their design, such as long-range control and stealth, in criminal investigations. The authors analyze methods for extracting forensically relevant data from USVs and their technical systems. The focus is on leveraging these features for law enforcement and forensic examination.

12crossref2026-09-01Irene Mejía Hurtado, Juan Camilo Angarita Noriega, Enric Cervera Mateu, Raúl Marín PradesDOI ↗ (opens in a new tab)

Land–Maritime Localization in Autonomous Robotics: Nav2 versus EasyNav (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.

13openalex2026-09-03Muhammad Fadhli Makhtar, Mohd Nizam Ab Rahman, Iliani Mohd Ikram, Mohd Hafiz Abdul Majid +2 moreDOI ↗ (opens in a new tab)

Design and Implementation of a Digitalised Marine Aids to Navigation Monitoring System for High‐Density Maritime Traffic Waters: A Case Study of the Strait of Malacca (opens in a new tab)

This study designed and deployed the AtoN Remote Monitoring System (ARMS) in the Strait of Malacca to improve monitoring of marine aids to navigation in high-traffic waters. The system uses multi-sensor technology, hybrid communication (AIS, VHF, cellular), and a central platform, achieving 98.3% monthly availability and 98.7% communication success over a 90-day test across 478 installations. Fault detection was significantly faster (8.4 minutes) than traditional methods (15–45 days), with a 93.9% alarm accuracy rate. Results confirm the system’s effectiveness for real-time, condition-based management of maritime infrastructure.

18openalex2026-09-01Orfeas Bourchas, Ioannis Karlatiras, George PapalambrouDOI ↗ (opens in a new tab)

A novel application of attention U-Net for marine biofouling segmentation (opens in a new tab)

This study introduces an Attention U-Net model for segmenting marine biofouling in real-world conditions, enhancing traditional U-Net with spatial attention gates and squeeze-and-excitation modules. The model was trained and tested on 504 annotated images from ship hull surveys under varied environmental conditions. It achieved a validation Dice coefficient of 0.814 and a macro-accuracy of 0.689. The approach aims to improve automated inspection systems, reducing manual effort and increasing detection accuracy in maritime maintenance.

22crossref2026-09-04Oren GalDOI ↗ (opens in a new tab)

Adaptive Unmanned Surface Vehicles swarm optimization for target tracking in dynamic environments (opens in a new tab)

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.

23crossref2026-09-03Do Khac Tiep, Duc‐Anh PhamDOI ↗ (opens in a new tab)

Adaptive Fault‐Tolerant Prescribed Performance Control for Underactuated Unmanned Surface Vehicles (opens in a new tab)

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.

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