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Autonomous Marine Vehicle Operations—3rd Edition
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Autonomous Marine Vehicle Operations—3rd Edition

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: 25 March 2026 | Viewed by 8049

Special Issue Editors

Prof. Dr. Xiao Liang

E-Mail Website
Guest Editor
Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian 116026, China
Interests: decision-making and advanced control; unmanned technology and swarm intelligence in maritime applications; autonomous surface vehicles; autonomous underwater vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rubo Zhang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, China
Interests: intelligent robot hardware and software architecture; task planning; path planning; multi-robot technology; autonomous decision-making technology in complex environments
Special Issues, Collections and Topics in MDPI journals
Dr. Xingru Qu

E-Mail
Guest Editor
College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, China
Interests: autonomous marine vehicles (underwater and surface); guidance and control; coordination
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal of Marine Science and Engineering is pleased to announce a Special Issue entitled “Autonomous Marine Vehicle Operations—3rd Edition”, based on the great success of our previous Special Issue with the same title.

The world has witnessed a rapid development of unmanned systems, which has paved the way for innovative approaches to previously unsolvable problems in marine and ocean engineering. Advanced and intelligent operation methods of marine vehicles are being applied to a variety of significant engineering applications, contributing to successful interdisciplinary cooperation. This edition of the Special Issue on marine vehicle operation invites submissions of the latest experimental and simulation studies related to the perception, decision-making, and control of marine vehicles. The Guest Editors of this Special Issue, together with the Editors of the Journal of Marine Science and Engineering, will provide a high-quality review process and ensure the efficient publication of your original research and review articles on the following topics:

  • Water surface object detection and recognition;
  • Underwater vision and identification;
  • Marine vehicle navigation, guidance, and control;
  • Path planning, path following, and trajectory tracking;
  • Collision avoidance and obstacle avoidance;
  • Coordination and game for marine vehicles;
  • Fault diagnosis design and fault tolerant control;
  • Marine vehicle modelling and simulation technologies;
  • Propulsion systems and energy efficiency;
  • Maritime safety and risk assessment.

Prof. Dr. Xiao Liang
Prof. Dr. Rubo Zhang
Dr. Xingru Qu
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 monthly 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

  • autonomous operations
  • surface and underwater applications
  • perception
  • decision-making
  • control
  • coordination and game
  • safety and efficiency

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Related Special Issues

Published Papers (5 papers)

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Research

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22 pages, 1821 KB  
Article
A Novel Cooperative Navigation Algorithm Based on Factor Graph and Lie Group for AUVs
by Jiapeng Liu, Xiaodong Bu and Chao Wu
J. Mar. Sci. Eng. 2025, 13(10), 1988; https://doi.org/10.3390/jmse13101988 - 16 Oct 2025
Viewed by 98
Abstract
Traditional cooperative navigation algorithms for multiple AUVs are typically designed for a single specific configuration, such as parallel or leader-slave. This paper proposes a novel cooperative navigation algorithm based on factor graph and Lie group to address the multi-AUV localization problem, which is [...] Read more.
Traditional cooperative navigation algorithms for multiple AUVs are typically designed for a single specific configuration, such as parallel or leader-slave. This paper proposes a novel cooperative navigation algorithm based on factor graph and Lie group to address the multi-AUV localization problem, which is applicable to various multi-AUV configurations. First, the motion state of an AUV is represented within the two-dimensional special Euclidean group (SE(2)) space from Lie theory. Second, the motion of the AUV and acoustic-based range and bearing measurements are modeled to derive the motion error function and the range and bearing error function, respectively. Depending on the formulation of the motion error function, the proposed approach comprises two methods: Method 1 and Method 2. Third, the Gauss-Newton method is employed for nonlinear optimization to obtain the optimal estimates of the motion states for all AUVs. Finally, a parameter-level simulation system for AUV cooperative navigation is established to evaluate the algorithm’s performance under two different multi-AUV configurations. Method 1 is designed for parallel configurations, reducing the average RMSE of position and orientation errors by 29% compared to the EKF. Method 2 is tailored for leader-slave configurations, reducing the average RMSE of position and orientation errors by 38% compared to the EKF. Simulation results demonstrate that the proposed algorithm achieves superior performance across different AUV configurations compared to conventional EKF-based approaches. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—3rd Edition)
28 pages, 5663 KB  
Article
Quasi-Infinite Horizon Nonlinear Model Predictive Control for Cooperative Formation Tracking of Underactuated USVs with Four Degrees of Freedom
by Meng Yang, Ruonan Li, Hao Wang, Wangsheng Liu and Zaopeng Dong
J. Mar. Sci. Eng. 2025, 13(9), 1812; https://doi.org/10.3390/jmse13091812 - 19 Sep 2025
Viewed by 513
Abstract
To address the issues of external unknown disturbances and roll motion in the tracking control of underactuated unmanned surface vehicle (USV) formation, a cooperative formation control method based on nonlinear model predictive control (NMPC) algorithm and finite-time disturbance observer is proposed. Initially, a [...] Read more.
To address the issues of external unknown disturbances and roll motion in the tracking control of underactuated unmanned surface vehicle (USV) formation, a cooperative formation control method based on nonlinear model predictive control (NMPC) algorithm and finite-time disturbance observer is proposed. Initially, a tracking error model for the USV formation is established within a leader–follower framework, utilizing a four-degree-of-freedom (4-DOF) dynamic model to simultaneously account for roll motion and trajectory tracking. This error model is then approximately linearized and discretized. To mitigate the initial non-smoothness in the desired trajectories of the follower USVs, a tracking differentiator is designed to smooth the heading angle of the leader USV. Thereafter, a quasi-infinite horizon NMPC algorithm is developed, in which a terminal penalty function is constructed based on quasi-infinite horizon theory. Furthermore, a finite-time disturbance observer is developed to facilitate real-time estimation and compensation for unknown marine disturbances. The proposed method’s effectiveness is validated both mathematically and in simulation. Mathematically, closed-loop stability is rigorously guaranteed via a Lyapunov-based proof of the quasi-infinite horizon NMPC design. In simulations, the algorithm demonstrates superior performance, reducing steady-state tracking errors by over 80% and shortening convergence times by up to 75% compared to a conventional PID controller. These results confirm the method’s robustness and high performance for complex USV formation tasks. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—3rd Edition)
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35 pages, 8275 KB  
Article
Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning
by Charilaos Latinopoulos, Efstathios Zavvos, Dimitrios Kaklis, Veerle Leemen and Aristides Halatsis
J. Mar. Sci. Eng. 2025, 13(5), 902; https://doi.org/10.3390/jmse13050902 - 30 Apr 2025
Cited by 2 | Viewed by 4110
Abstract
Marine voyage optimization determines the optimal route and speed to ensure timely arrival. The problem becomes particularly complex when incorporating a dynamic environment, such as future expected weather conditions along the route and unexpected disruptions. This study explores two model-free Deep Reinforcement Learning [...] Read more.
Marine voyage optimization determines the optimal route and speed to ensure timely arrival. The problem becomes particularly complex when incorporating a dynamic environment, such as future expected weather conditions along the route and unexpected disruptions. This study explores two model-free Deep Reinforcement Learning (DRL) algorithms: (i) a Double Deep Q Network (DDQN) and (ii) a Deep Deterministic Policy Gradient (DDPG). These algorithms are computationally costly, so we split optimization into an offline phase (costly pre-training for a route) and an online phase where the algorithms are fine-tuned as updated weather data become available. Fine tuning is quick enough for en-route adjustments and for updating the offline planning for different dates where the weather might be very different. The models are compared to classical and heuristic methods: the DDPG achieved a 4% lower fuel consumption than the DDQN and was only outperformed by Tabu Search by 1%. Both DRL models demonstrate high adaptability to dynamic weather updates, achieving up to 12% improvement in fuel consumption compared to the distance-based baseline model. Additionally, they are non-graph-based and self-learning, making them more straightforward to extend and integrate into future digital twin-driven autonomous solutions, compared to traditional approaches. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—3rd Edition)
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20 pages, 3873 KB  
Article
Neural Unilateral Nussbaum Gain Sliding Mode Control for Uncertain Ship Course Keeping with an Unknown Control Direction
by Guoxin Ma, Dongliang Li, Qiang Wei and Lei Song
J. Mar. Sci. Eng. 2025, 13(5), 846; https://doi.org/10.3390/jmse13050846 - 24 Apr 2025
Cited by 1 | Viewed by 405
Abstract
This paper focuses on the ship control system and studies the problem of unknown control directions. Considering that the traditional Nussbaum gain method has to consider the complex situation where the gain converges to both positive and negative infinity when proving the stability [...] Read more.
This paper focuses on the ship control system and studies the problem of unknown control directions. Considering that the traditional Nussbaum gain method has to consider the complex situation where the gain converges to both positive and negative infinity when proving the stability of a system, a unilateral Nussbaum function is defined in this paper. By constructing this function, the design and proof process of the adaptive Nussbaum gain method are simplified. Taking the ship course–keeping control system as the research object, a course angle tracking controller is designed by combining neural network, robust adaptive, and sliding mode control techniques. A dual-input RBF single-output neural network is used to approximate the uncertain part of the system, and the robust adaptive control is adopted to deal with the unknown disturbance. The simulation results at the end of the article show that when the direction suddenly switches, the overshoot of the system reaches 40%, and the adjustment time is approximately 3 s. However, the system can still adapt to the change of the control direction and maintain stability, indicating that the method proposed in this paper is reasonable and effective. And the proposed method can effectively cope with the problems of the unknown control direction and its jump, keeping the system stable, which has great theoretical and engineering application value. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—3rd Edition)
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Review

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20 pages, 3231 KB  
Review
An Overview of Recent Advances in Pursuit–Evasion Games with Unmanned Surface Vehicles
by Xingru Qu, Linghui Zeng, Shihang Qu, Feifei Long and Rubo Zhang
J. Mar. Sci. Eng. 2025, 13(3), 458; https://doi.org/10.3390/jmse13030458 - 27 Feb 2025
Cited by 4 | Viewed by 2204
Abstract
With the rapid development of perception, decision-making, and control technologies, pursuit–evasion (PE) games with unmanned surface vehicles (USVs) have become an interesting research topic in military implementations and civilian areas. In this paper, we provide an overview of recent advances in the PE [...] Read more.
With the rapid development of perception, decision-making, and control technologies, pursuit–evasion (PE) games with unmanned surface vehicles (USVs) have become an interesting research topic in military implementations and civilian areas. In this paper, we provide an overview of recent advances in the PE games with USVs. First, the motion model of USVs and successful criteria for PE games are presented. Next, some challenging issues in PE games with USVs are briefly discussed. Then, recent results on one-pursuer one-evader, multiple-pursuer one-evader, and multiple-pursuer multiple-evader with USVs are reviewed in detail. Finally, several theoretical and technical issues are suggested to direct future research, including target prediction, dynamic task allocation, brain-inspired decision-making, safe control, and PE experiments. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—3rd Edition)
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