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Advances in Modelling, Navigation, and Intelligent Control of Marine Vehicles...
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Advances in Modelling, Navigation, and Intelligent Control of Marine Vehicles and Robotics

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (25 March 2026) | Viewed by 2443

Editor

Dr. Mai The Vu

E-Mail Website
Guest Editor
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05000, Republic of Korea
Interests: system dynamics; mechatronics; underwater vehicles; automation and robotics; trajectory tracking; path planning; multi-body dynamic modelling; intelligent navigation; autonomous vehicles; machine learning

Special Issue Information

Dear Colleagues,

You are cordially invited to submit manuscripts for a Special Issue of Journal of Marine Science and Engineering (JMSE) titled “Advances in Modelling, Navigation, and Intelligent Control of Marine Vehicles and Robotics.”

In recent years, autonomous and robotic marine systems have gained significant research attention due to their critical roles in ocean exploration, environmental monitoring, offshore operations, defence, and maritime logistics. These systems include autonomous surface vehicles (ASVs), autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), and hybrid robotic platforms capable of operating in complex marine environments. Research in this area focuses on designing, developing, and supporting the operation of unmanned marine vehicles for scientific, academic, and commercial applications. In particular, modelling, control, and navigation techniques are essential for constructing efficient and reliable systems that ensure safety and operational effectiveness.

This Special Issue aims to gather contributions on modelling, control, guidance, state estimation, cooperation, and localization of marine vehicles and robotics. We welcome original research articles and comprehensive reviews that consolidate current knowledge, highlight recent theoretical and experimental advancements, and discuss practical applications and emerging challenges. Contributions that explore novel techniques with potential real-world impact are especially encouraged.

Potential topics of interest include, but are not limited to, the following:

  • Autonomous surface and underwater vehicles: ASVs, USVs, AUVs, ROVs, UUVs, and underwater gliders.
  • Swarms of marine vehicles, multi-vehicle mission planning, and cooperative operations.
  • Guidance, navigation, and path/trajectory planning, including collision avoidance.
  • Kinematics, vehicle dynamics, and dynamic positioning systems.
  • Sensor and actuator systems, including new active and passive sensors.
  • Control, intelligent/adaptive architectures, and cooperative control for multi-unmanned systems.
  • Modelling, simulation, and computational fluid dynamics (CFD) applications.
  • Structural modelling, analysis, and vehicle model testing.
  • Applications of artificial intelligence (AI) and machine learning (ML) in marine robotics and autonomous networks.
  • Power management, fault diagnosis, and fault-tolerant control.
  • Field trials, case studies, and experimental validations.

We look forward to receiving your contributions to this Special Issue of JMSE, which will bring together the latest developments in this field.

Dr. Mai The Vu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned surface vehicles (USVs)
  • autonomous surface vehicles (ASVs)
  • remotely operated vehicles (ROVs)
  • unmanned underwater vehicles (UUVs)
  • glider
  • autonomous underwater vehicles (AUVs)
  • navigation and guidance system
  • intelligent control
  • robust control
  • linear and nonlinear control
  • formation control
  • adaptive control
  • path planning and path following
  • machine learning and deep learning
  • localization, mapping and SLAM

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

31 pages, 17152 KB  
Article
CD-HSSRL: Cross-Domain Hierarchical Safe Switching Reinforcement Learning Framework for Autonomous Amphibious Robot Navigation
by Shuang Liu, Lei Wei and Xiaoqing Li
J. Mar. Sci. Eng. 2026, 14(9), 859; https://doi.org/10.3390/jmse14090859 - 3 May 2026
Viewed by 485
Abstract
Autonomous tracked amphibious robotic systems operating across water and land environments are essential for coastal inspection, disaster response, environmental monitoring, and complex terrain exploration. However, discontinuous water–land dynamics, unstable medium switching, and safety-critical control under environmental uncertainty pose significant challenges to existing amphibious [...] Read more.
Autonomous tracked amphibious robotic systems operating across water and land environments are essential for coastal inspection, disaster response, environmental monitoring, and complex terrain exploration. However, discontinuous water–land dynamics, unstable medium switching, and safety-critical control under environmental uncertainty pose significant challenges to existing amphibious navigation and path planning methods, where global reachability and adaptive decision-making are difficult to unify. Motivated by these challenges, this paper proposes CD-HSSRL, a Cross-Domain Hierarchical Safe-Switching Reinforcement Learning framework for autonomous tracked amphibious navigation. Specifically, a Cross-Domain Global Reachability Planner is developed to construct unified cost representations across heterogeneous water–land environments, a Hierarchical Safe Switching Policy enables stable medium-transition decision-making through option-based policy decomposition with switching regularization, and a Safety-Constrained Continuous Controller integrates action safety projection and risk-sensitive reward shaping to ensure collision-free control during complex shoreline interactions. These components are jointly optimized to achieve robust cross-domain navigation. The experimental results in the Gazebo + UUV simulation environment show that the proposed method demonstrates competitive performance compared with baseline approaches, achieving higher success rates and lower collision rates across water, land, and transition environments. In particular, in cross-domain scenarios, the proposed method improves success rates by approximately 20% compared to conventional RL methods while maintaining stable performance under environmental disturbances. Robustness and ablation studies further verify the effectiveness of hierarchical switching and safety-constrained control mechanisms. Overall, this work establishes an integrated framework for safe and robust cross-domain navigation of tracked amphibious robotic systems, providing new insights into hierarchical safe-switching architectures for multi-medium autonomous robots. Full article
(This article belongs to the Special Issue Advances in Modelling, Navigation, and Intelligent Control of Marine Vehicles and Robotics)
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30 pages, 7114 KB  
Article
An Adaptive Impedance Control Method for Underwater Dexterous Hands Based on Reinforcement Learning
by Yuze Sun, Qingfeng Yao, Qiyan Tian and Naizhi He
J. Mar. Sci. Eng. 2026, 14(8), 715; https://doi.org/10.3390/jmse14080715 - 12 Apr 2026
Viewed by 740
Abstract
With the continuous advancement of marine development, underwater operational tasks are becoming increasingly diverse and complex. Addressing the limitations of traditional methods and intelligent planning—which focus solely on acquiring task skills while separating grasp planning from force planning—this paper proposes a modeling approach [...] Read more.
With the continuous advancement of marine development, underwater operational tasks are becoming increasingly diverse and complex. Addressing the limitations of traditional methods and intelligent planning—which focus solely on acquiring task skills while separating grasp planning from force planning—this paper proposes a modeling approach integrating impedance control with deep reinforcement learning. Using a five-finger humanoid underwater dexterous hand as the grasping execution platform, this method achieves collaborative decision-making between grasp planning and force control for underwater dexterous hands. First, a modular underwater dexterous grasping scenario is established. Its kinematic model and inverse solution are analyzed, and the grasping problem is modeled as a Markov decision process. Second, based on the dexterous fingertip impedance control model for simulation, a grasping strategy learning method grounded in deep reinforcement learning is constructed to address the complex control challenges posed by the high degrees of freedom of the dexterous manipulator. Finally, the Proximal Policy Optimization (PPO) algorithm is employed for grasping strategy learning. An underwater dexterous grasping parallel training and testing environment is established using the Isaac Lab simulation platform to rapidly validate the learning method. Simulation results demonstrate the proposed method’s excellent dexterous compliant control performance and strong robustness to underwater variable environments: the PPO-based impedance control scheme reduces contact force variance by 56% compared to pure position control. The average maximum contact force is suppressed to 3.26 N, representing a 60.4% reduction compared to pure position control. This study achieves the organic integration of underwater hydrodynamic compensation, adaptive impedance control, and grasping strategy learning, providing a novel and effective solution for compliant grasping control of underwater dexterous manipulators. Full article
(This article belongs to the Special Issue Advances in Modelling, Navigation, and Intelligent Control of Marine Vehicles and Robotics)
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23 pages, 3728 KB  
Article
Fault-Tolerant Optimization Algorithm for Ship-Integrated Navigation Systems Based on Perceptual Information Compensation
by Daheng Zhang, Xuehao Zhang, Weibo Wang and Muzhuang Guo
J. Mar. Sci. Eng. 2026, 14(3), 293; https://doi.org/10.3390/jmse14030293 - 2 Feb 2026
Viewed by 690
Abstract
Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a [...] Read more.
Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a compass (SINS/DVL/COMPASS) can provide essential state information, but the accuracy and fault tolerance of such systems are constrained by weak observability of position/heading errors and strong dependence on DVL measurements. This study proposes a fault-tolerant optimization method based on perceptual information compensation. First, radar imagery and electronic chart data are fused at the feature level using a weighted wavelet strategy to enhance the environmental feature saliency for shoreline extraction. Second, characteristic coastline inflection points are detected and tracked using a dual-curvature and distance-constrained procedure, generating external position observations via radar–chart matching. These observations are incorporated into the SINS/DVL/COMPASS framework to improve its state observability and robustness. Simulation results show that under nominal conditions, perceptual compensation mitigates error divergence and promotes the convergence of position errors, improving the positioning stability. In terms of robustness, the proposed method delivered more stable state-error behavior than the baseline under DVL speed faults of +2 m/s, −2 m/s, and +2 m/s injected at 301–330, 701–730, and 1101–1130 s, respectively. Quantitatively, the 3σ bounds of velocity and position-related errors are reduced under fault conditions, indicating improved fault tolerance and suitability for short-term nearshore autonomous navigation during GNSS outages. Full article
(This article belongs to the Special Issue Advances in Modelling, Navigation, and Intelligent Control of Marine Vehicles and Robotics)
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