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Unmanned Marine Vehicles: Navigation, Control and Sensing
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Unmanned Marine Vehicles: Navigation, Control and Sensing

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: 30 July 2024 | Viewed by 1566

Special Issue Editors

Prof. Dr. Tieshan Li

E-Mail Website
Guest Editor
School of Automation Engineeering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: intelligent control for complex systems; modeling and control of marine vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Liying Hao

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China
Interests: robust fault-tolerant control; sliding-mode control; model predictive control; deep learning with an emphasis on applications in marine vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of the successful application of advanced control and artificial intelligence techniques in unmanned marine vehicles, significant progresses have been made in the fields of navigation, control, and sensing. Recent advances in unmanned marine vehicles have demonstrated their great potential to transform our ways of monitoring, intervening, exploring, and utilizing the marine environment, from the sea surface down to the deepest depths and furthest reaches of the oceans.

This Special Issue is seeking high-quality original contributions: technical papers that address the main research challenges related to the navigation, control, and sensing of marine vehicle systems. Papers are invited on topics including (but not limited to) the following:

  • The navigation and advanced control of marine vehicle systems;
  • The localization and navigation of marine vehicle systems;
  • The perception and motion planning of marine vehicle systems;
  • The stability and robustness analysis of marine vehicle systems;
  • Learning and artificial intelligence (AI) in marine vehicle systems;
  • Sensor fusion in autonomous marine vehicle systems;
  • The cooperative and coordinated control of autonomous marine vehicle systems;
  • Energy and power management in autonomous marine vehicle systems;
  • Fault diagnosis and the fault-tolerant control of marine vehicle systems;
  • Robust model-predictive control of marine vehicle systems;
  • Identification and estimation in autonomous marine vehicle systems;
  • Safety and security control of marine vehicle systems;
  • Simulations and case studies of applications with autonomous marine vehicle systems.

Prof. Dr. Tieshan Li
Dr. Liying Hao
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

  • unmanned marine vehicles
  • navigation
  • control
  • sensing

Published Papers (3 papers)

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Research

22 pages, 1457 KiB  
Article
Integral Sliding Mode Output Feedback Control for Unmanned Marine Vehicles Using T–S Fuzzy Model with Unknown Premise Variables and Actuator Faults
by Yang Wang, Xin Yang, Liying Hao, Tieshan Li and C. L. (Philip) Chen
J. Mar. Sci. Eng. 2024, 12(6), 920; https://doi.org/10.3390/jmse12060920 (registering DOI) - 30 May 2024
Abstract
This paper addresses integral sliding mode output feedback fault-tolerant control (FTC) of unmanned marine vessels (UMVs) with unknown premise variables and actuator faults. Due to the complexity of the marine environment, the presence of uncertainties in the yaw angle renders the premise variables [...] Read more.
This paper addresses integral sliding mode output feedback fault-tolerant control (FTC) of unmanned marine vessels (UMVs) with unknown premise variables and actuator faults. Due to the complexity of the marine environment, the presence of uncertainties in the yaw angle renders the premise variables in the Takagi–Sugeno (T–S) fuzzy model of UMVs unknown. Consequently, traditional integral sliding mode techniques become infeasible. To address this issue, a control strategy combining integral sliding mode based on output feedback with a compensator utilizing switching mechanisms is proposed. First, a radial basis function neural network is used to approximate the nonlinear terms in the UMV T–S fuzzy model. In addition, an integral sliding mode surface is constructed based on fault estimation information and membership function estimation. On this basis, an FTC scheme based on integral sliding mode output feedback is developed to ensure that the UMV system is asymptotically stable and satisfies the prescribed H∞ performance index. Finally, simulation results are provided to demonstrate the effectiveness of the presented control strategy. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles: Navigation, Control and Sensing)
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20 pages, 3583 KiB  
Article
FSN-YOLO: Nearshore Vessel Detection via Fusing Receptive-Field Attention and Lightweight Network
by Na Du, Qing Feng, Qichuang Liu, Hui Li and Shikai Guo
J. Mar. Sci. Eng. 2024, 12(6), 871; https://doi.org/10.3390/jmse12060871 - 24 May 2024
Viewed by 323
Abstract
Vessel detection is critical for ensuring maritime transportation and navigational safety, creating a pressing need for detection methodologies that are more efficient, precise, and intelligent in the maritime domain. Nonetheless, accurately detecting vessels across multiple scales remains challenging due to the diversity in [...] Read more.
Vessel detection is critical for ensuring maritime transportation and navigational safety, creating a pressing need for detection methodologies that are more efficient, precise, and intelligent in the maritime domain. Nonetheless, accurately detecting vessels across multiple scales remains challenging due to the diversity in vessel types and locations, similarities in ship hull shapes, and disturbances from complex environmental conditions. To address these issues, we introduce an innovative FSN-YOLO framework that utilizes enhanced YOLOv8 with multi-layer attention feature fusion. Specifically, FSN-YOLO employs the backbone structure of FasterNet, enriching feature representations through super-resolution processing with a lightweight Convolutional Neural Network (CNN), thereby achieving a balance between processing speed and model size without compromising accuracy. Furthermore, FSN-YOLO uses the Receptive-Field Attention (RFA) mechanism to adaptively fine-tune the feature responses between channels, significantly boosting the network’s capacity to capture critical information and, in turn, improve the model’s overall performance and enrich the discriminative feature representation of ships. Experimental validation on the Seaship7000 dataset showed that, compared to the baseline YOLOv8l approach, FSN-YOLO considerably increased accuracy, recall rates, and [email protected]:0.95 by absolute margins of 0.82%, 1.54%, and 1.56%, respectively, positioning it at the forefront of current state-of-the-art models. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles: Navigation, Control and Sensing)
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15 pages, 3672 KiB  
Article
Research on the Influencing Factors of AUV Hovering Control in Null-Speed State
by Jianguo Wang, Chunmeng Jiang, Lei Wan, Yimei Zhou, Gangyi Hu, Xide Cheng and Gongxing Wu
J. Mar. Sci. Eng. 2024, 12(5), 725; https://doi.org/10.3390/jmse12050725 - 27 Apr 2024
Viewed by 482
Abstract
Intelligent underwater vehicles hover by way of a hovering control system. To provide design inputs and maneuver guidance, this study focused on the characteristics of intelligent underwater vehicles during hovering control with the propulsion system shut down, established a mathematical model of hovering [...] Read more.
Intelligent underwater vehicles hover by way of a hovering control system. To provide design inputs and maneuver guidance, this study focused on the characteristics of intelligent underwater vehicles during hovering control with the propulsion system shut down, established a mathematical model of hovering control and determined injection and drainage functions based on optimal control theory. From analysis simulation experiments, the influence laws of control parameters, control timing and rate of injection and drainage control upon hovering control were deduced. It is proposed that, at the time of control parameter selection, the continuous injection and drainage rate at each time should be reduced as far as possible to relieve the demand on the volume of the reservoir when the requirement of depth control accuracy has been satisfied. In addition, the injection and drainage control should initiate when depth changes exceed 0.5 m. Suggestions are included on the minimum injection and drainage rate required for different initial disturbances. The proposed suggestions guide the design of hovering control systems and hovering control over intelligent underwater vehicles. Full article
(This article belongs to the Special Issue Unmanned Marine Vehicles: Navigation, Control and Sensing)
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