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Autonomous Marine Vehicle Operations—2nd Edition
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Autonomous Marine Vehicle Operations—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: 20 August 2024 | Viewed by 3803

Special Issue Editors

Prof. Dr. Xiao Liang

E-Mail Website
Guest Editor
School of Naval Architecture and Ocean Engineering, 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 Website
Guest Editor
School 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 world has witnessed a rapid development of unmanned system, 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, ‘Autonomous Marine Vehicle Operations—2nd Edition’, invites submissions of latest experimental and simulation studies related to 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 reviewing process and ensure 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

Related Special Issue

Published Papers (4 papers)

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Research

16 pages, 2946 KiB  
Article
Quantification of Expert Knowledge in Describing COLREGs Linguistic Variables
by Miho Kristić and Srđan Žuškin
J. Mar. Sci. Eng. 2024, 12(6), 849; https://doi.org/10.3390/jmse12060849 - 21 May 2024
Viewed by 440
Abstract
The International Regulations for Preventing Collisions at Sea 1972 (COLREGs) have been the cornerstone of maritime navigation since their introduction. Knowledge and implementation of these rules are paramount in collision avoidance at sea. However, terms found in these rules are sometimes imprecise or [...] Read more.
The International Regulations for Preventing Collisions at Sea 1972 (COLREGs) have been the cornerstone of maritime navigation since their introduction. Knowledge and implementation of these rules are paramount in collision avoidance at sea. However, terms found in these rules are sometimes imprecise or fuzzy, as they are written by humans for humans, giving them some freedom in interpretation. The term Very Large Ship used in Rule 7 of the COLREGs is, by its nature, fuzzy. While human navigators understand this term’s meaning, it could be challenging for machines or autonomous ships to understand such an unprecise expression. Fuzzy sets could easily describe unprecise terms used in maritime navigation. A fuzzy set consists of elements with degrees of membership in a set, making them perfect for interpreting some terms where boundaries are unclear. This research was conducted among 220 navigational experts to describe linguistic variables used in maritime regulations. This research consists of an internationally distributed questionnaire. Membership data were collected with the adapted horizontal method, and the results were statistically analyzed, followed by regression analyses to describe the range and shape of membership functions. A conceptual model of the implementation of linguistic variables is presented. The novelty of this study derives from the data collecting, modeling, and quantification of the important but neglected linguistic term Very Large Ship based on a large number of navigational experts. The same quantification method could be easily used for other COLREGs linguistic variables, which could easily lift barriers to advances in intelligent solutions based on fuzzy sets. The obtained quantified fuzzy sets can be used in decision support or control systems used by conventional or autonomous ships in the future. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—2nd Edition)
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22 pages, 10376 KiB  
Article
A Low-Cost and High-Precision Underwater Integrated Navigation System
by Jiapeng Liu, Te Yu, Chao Wu, Chang Zhou, Dihua Lu and Qingshan Zeng
J. Mar. Sci. Eng. 2024, 12(2), 200; https://doi.org/10.3390/jmse12020200 - 23 Jan 2024
Cited by 1 | Viewed by 901
Abstract
The traditional underwater integrated navigation system is based on an optical fiber gyroscope and Doppler Velocity Log, which is high-precision but also expensive, heavy, bulky and difficult to adapt to the development requirements of AUV swarm, intelligence and miniaturization. This paper proposes a [...] Read more.
The traditional underwater integrated navigation system is based on an optical fiber gyroscope and Doppler Velocity Log, which is high-precision but also expensive, heavy, bulky and difficult to adapt to the development requirements of AUV swarm, intelligence and miniaturization. This paper proposes a low-cost, light-weight, small-volume and low-computation underwater integrated navigation system based on MEMS IMU/DVL/USBL. First, according to the motion formula of AUV, a five-dimensional state equation of the system was established, whose dimension was far less than that of the traditional. Second, the main source of error was considered. As the velocity observation value of the system, the velocity measured by DVL eliminated the scale error and lever arm error. As the position observation value of the system, the position measured by USBL eliminated the lever arm error. Third, to solve the issue of inconsistent observation frequencies between DVL and USBL, a sequential filter was proposed to update the extended Kalman filter. Finally, through selecting the sensor equipment and conducting two lake experiments with total voyages of 5.02 km and 3.2 km, respectively, the correctness and practicality of the system were confirmed by the results. By comparing the output of the integrated navigation system and the data of RTK GPS, the average position error was 4.12 m, the maximum position error was 8.53 m, the average velocity error was 0.027 m/s and the average yaw error was 1.41°, whose precision is as high as that of an optical fiber gyroscope and Doppler Velocity Log integrated navigation system, but the price is less than half of that. The experimental results show that the proposed underwater integrated navigation system could realize the high-precision and long-term navigation of AUV in the designated area, which had great potential for both military and civilian applications. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—2nd Edition)
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18 pages, 696 KiB  
Article
A Formation Control and Obstacle Avoidance Method for Multiple Unmanned Surface Vehicles
by Guanqun Liu, Naifeng Wen, Feifei Long and Rubo Zhang
J. Mar. Sci. Eng. 2023, 11(12), 2346; https://doi.org/10.3390/jmse11122346 - 12 Dec 2023
Viewed by 963
Abstract
This study introduces a method for formation control and obstacle avoidance for multiple unmanned surface vehicles (USVs) by combining an artificial potential field with the virtual structure method. The approach involves a leader–follower formation structure, where the leader autonomously avoids collisions using an [...] Read more.
This study introduces a method for formation control and obstacle avoidance for multiple unmanned surface vehicles (USVs) by combining an artificial potential field with the virtual structure method. The approach involves a leader–follower formation structure, where the leader autonomously avoids collisions using an artificial potential field based on the target’s position as a reference. It also determines the ideal position of each follower in the formation based on its own position, heading angle, and the formation structure. To effectively avoid obstacles and maintain formation, the follower selects the position of the target or its ideal position as a reference during movement, depending on whether it is being repelled by obstacles. Additionally, this paper modifies the attractive force model of the traditional artificial potential field method to restrict the maximum magnitude of the attractive force when encountering repulsive forces, thus expediting departure from obstacle areas. The dynamic characteristics of USVs are taken into account by constraining the maximum linear speed and angular speed. Formation stability is ensured by maintaining a constant speed for the leader, while the linear speed of the follower varies based on the distance to the reference object during movement. Simulation experiments demonstrated that this method can effectively avoid obstacles and maintain formation. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—2nd Edition)
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21 pages, 7023 KiB  
Article
COLREGs-Based Path Planning for USVs Using the Deep Reinforcement Learning Strategy
by Naifeng Wen, Yundong Long, Rubo Zhang, Guanqun Liu, Wenjie Wan and Dian Jiao
J. Mar. Sci. Eng. 2023, 11(12), 2334; https://doi.org/10.3390/jmse11122334 - 11 Dec 2023
Cited by 1 | Viewed by 954
Abstract
This research introduces a two-stage deep reinforcement learning approach for the cooperative path planning of unmanned surface vehicles (USVs). The method is designed to address cooperative collision-avoidance path planning while adhering to the International Regulations for Preventing Collisions at Sea (COLREGs) and considering [...] Read more.
This research introduces a two-stage deep reinforcement learning approach for the cooperative path planning of unmanned surface vehicles (USVs). The method is designed to address cooperative collision-avoidance path planning while adhering to the International Regulations for Preventing Collisions at Sea (COLREGs) and considering the collision-avoidance problem within the USV fleet and between USVs and target ships (TSs). To achieve this, the study presents a dual COLREGs-compliant action-selection strategy to effectively manage the vessel-avoidance problem. Firstly, we construct a COLREGs-compliant action-evaluation network that utilizes a deep learning network trained on pre-recorded TS avoidance trajectories by USVs in compliance with COLREGs. Then, the COLREGs-compliant reward-function-based action-selection network is proposed by considering various TS encountering scenarios. Consequently, the results of the two networks are fused to select actions for cooperative path-planning processes. The path-planning model is established using the multi-agent proximal policy optimization (MAPPO) method. The action space, observation space, and reward function are tailored for the policy network. Additionally, a TS detection method is introduced to detect the motion intentions of TSs. The study conducted Monte Carlo simulations to demonstrate the strong performance of the planning method. Furthermore, experiments focusing on COLREGs-based TS avoidance were carried out to validate the feasibility of the approach. The proposed TS detection model exhibited robust performance within the defined task. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—2nd Edition)
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