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Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition
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Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd 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: 5 June 2025 | Viewed by 985

Special Issue Editors

Prof. Dr. Yunsheng Fan

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: unmanned ships; intelligent navigation; autonomous control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yan Yan

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: guidance; navigation and control of marine vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Dongdong Mu

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: unmanned surface vehicle; intelligent decision; nonlinear control
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 “Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition”, which is based on the great success of our previous Special Issue with the same title.

Marine transportation and traffic are pivotal aspects of global connectivity, drawing significant global attention within the research domain. Within this field, unmanned marine vehicles emerge as crucial assets, including ships, boats, underwater vehicles, underwater gliders, etc. These vehicles are often empowered by advancements in navigation, control, and sensing technologies, and have the potential to redefine the possibilities of marine exploration and operations. This Special Issue serves as a platform for the latest advancements in this dynamic field.

Authors are invited to contribute research focusing on perception technologies, planning algorithms, control strategies, and sensing approaches tailored for unmanned marine vehicles. This collection aims to showcase interdisciplinary efforts in order to drive innovation in autonomous marine systems. Topics of interest include, but are not limited to, the environmental perception of marine vehicles, the autonomous navigation and control of marine vehicles, multi-agent technologies, and efficiency assessment and testing.

Moreover, this Special Issue will play a crucial role in intelligent maritime navigation, which is undoubtedly one of the most focal topics in marine research. This defines the future direction of marine research and promotes the development of various industries and fields.

It aims to explore the advancements, challenges, and applications of unmanned autonomous maritime vehicles, as well to provide insights into their role in enhancing efficiency, safety, intelligence level, and sustainability in maritime operations.

The development of unmanned autonomous maritime vehicles traces back to the late 20th century, with early prototypes primarily being remote-controlled. Over time, advancements in technology, particularly in artificial intelligence, sensor systems, and communication networks, have propelled these vehicles into sophisticated autonomous systems capable of independently executing complex missions.

Cutting-edge research in this field includes enhancing the autonomy and intelligence of maritime vehicles through the use of machine learning and neural networks, thus improving their navigational capabilities in challenging environments and integrating novel sensor technologies for more precise data collection and analysis.

We are seeking original research papers that delve into the design, development, applications, and future prospects of unmanned autonomous maritime vehicles. Papers should offer valuable insights into technological innovations, and application challenges whilst emphasizing the broader impact of these vehicles on various areas relating to perception, planning, and control and swarm.

Prof. Dr. Yunsheng Fan
Prof. Dr. Yan Yan
Dr. Dongdong Mu
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

  • intelligent multi-agent systems
  • intelligent control of ships and ocean vehicles
  • intelligent control of underwater vehicles
  • perception and sensor fusion for marine vehicles
  • unmanned system game confrontation
  • image processing for ocean navigation
  • AI-based path planning and collision avoidance
  • hydrodynamic modeling and fluid dynamics
  • control of unmanned marine vehicles in special scenarios
  • field verification of unmanned marine vehicles
  • development trend of unmanned systems in the future
  • emerging technologies in maritime autonomy

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Published Papers (1 paper)

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Research

30 pages, 9151 KiB  
Article
Research on LSTM-PPO Obstacle Avoidance Algorithm and Training Environment for Unmanned Surface Vehicles
by Wangbin Luo, Xiang Wang, Fang Han, Zhiguo Zhou, Junyu Cai, Lin Zeng, Hong Chen, Jiawei Chen and Xuehua Zhou
J. Mar. Sci. Eng. 2025, 13(3), 479; https://doi.org/10.3390/jmse13030479 - 28 Feb 2025
Viewed by 718
Abstract
The current unmanned surface vehicle (USV) intelligent obstacle avoidance algorithm based on deep reinforcement learning usually adopts the mass point model to train in an ideal environment. However, in actual navigation, due to the influence of the ship model and the water surface [...] Read more.
The current unmanned surface vehicle (USV) intelligent obstacle avoidance algorithm based on deep reinforcement learning usually adopts the mass point model to train in an ideal environment. However, in actual navigation, due to the influence of the ship model and the water surface environment, the training set is triggered. The reward function does not match the actual situation, resulting in a poor obstacle avoidance effect. In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. We integrate the motion characteristics of the unmanned boat and the influencing factors of the surface environment, based on the curiosity-driven set reward function, to improve its autonomous obstacle avoidance ability, combined with the LSTM network to identify and save obstacle information to improve the adaptability to the unknown environment; virtual simulation is performed in Unity. The engine builds a USV physical model and a refined water deep reinforcement learning training environment including a variety of obstacle models. The experimental results demonstrate that the LSTM-PPO algorithm exhibits an effective and rational obstacle avoidance effect, with a success rate of 86.7%, an average path length of 198.52 m, and a convergence time of 1.5 h. A comparison with the performance of three other deep reinforcement learning algorithms reveals that the LSTM-PPO algorithm exhibits a 21.5% reduction in average convergence time, an 18.5% reduction in average path length, and an approximately 20% enhancement in the success rate of obstacle avoidance in complex environments. These results indicate that the LSTM-PPO algorithm can effectively enhance the search efficiency and optimize the path planning in obstacle avoidance for unmanned boats, rendering it more rational. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm—2nd Edition)
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