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Intelligent Measurement and Control System of Marine Robots
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Intelligent Measurement and Control System of Marine Robots

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: 10 July 2025 | Viewed by 6444

Special Issue Editors

Prof. Dr. Yimin Chen

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: environmental perception; navigation planning; motion control; autonomous underwater vehicles; underwater robots
Dr. Zhuo Zhang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: multiagent systems; distributed control; optimal control; fuzzy systems
Prof. Dr. Zhouhua Peng

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China
Interests: unmanned surface vehicle swarm control; embedded systems; power electronics technology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine robots are expected to operate in dangerous and risky ocean areas where it is not possible for human beings to do so. Therefore, in the coming few decades, marine robots will be required to be more intelligent, more efficient, and more reliable. This leads to extensive investigations of measurement and control science for improving the intelligence and capability of marine robots. However, there are still many challenges and questions to be addressed, including target detection, sonar image recognition, swarm cooperation, motion planning, signal processing, accurate navigation and control, etc. To alleviate these challenges, this Special Issue provides an opportunity to share knowledge on the development of marine robots in the fields of measurement and control systems. On this basis, marine robots will further improve their intelligent operation performances by exploiting their strength.

The aim of this Special Issue is to publish recent findings, solutions, and applications in the field of marine robots. Relevant technologies enhancing prototyping, simulation, dataset, and user experience are also desired.

Prof. Dr. Yimin Chen
Dr. Zhuo Zhang
Prof. Dr. Zhouhua Peng
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

  • kinematics and dynamics modeling
  • image recognition and signal processing
  • artificial neural networks
  • trajectory and motion planning
  • fault identification and detection
  • navigation and control system
  • communication systems
  • multi-sensor fusion and measurement
  • intelligent control and automation systems
  • swarm intelligence and evolutionary algorithms

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Published Papers (5 papers)

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Research

17 pages, 4941 KiB  
Article
Underwater Target Localization Method Based on Uniform Linear Electrode Array
by Wenjing Shang, Feixiang Gao, Jiahui Liu, Yunhe Pang, Sergey V. Volvenko, Vladimir M. Olshanskiy and Yidong Xu
J. Mar. Sci. Eng. 2025, 13(2), 306; https://doi.org/10.3390/jmse13020306 - 6 Feb 2025
Viewed by 804
Abstract
The underwater electric field signal can be excited by underwater vehicles, such as the shaft-rate electric field and the corrosion electric field. The electric field signature of each vehicle exhibits significant differences in time and frequency domain, which can be exploited to determine [...] Read more.
The underwater electric field signal can be excited by underwater vehicles, such as the shaft-rate electric field and the corrosion electric field. The electric field signature of each vehicle exhibits significant differences in time and frequency domain, which can be exploited to determine target positions. In this paper, a novel passive localization method for underwater targets is presented, leveraging a uniform linear electrode array (ULEA). The ULEA manifold along the axial direction is derived from the electric field propagation in an infinite lossy medium, which provides the nonlinear mapping relationship between the target position and the voltage data acquired by the ULEA. In order to locate the targets, the multiple signal classification (MUSIC) algorithm is applied. Then, capitalizing on the rotational invariance of matrix operations and exploiting the symmetry inherent in the ULEA, we streamline the six-dimensional spatial spectral scanning onto a two-dimensional plane, providing azimuth and distance information for the targets. This method significantly reduces computational overhead. To validate the efficacy of our proposed method, we devise a localization system and conduct a simulation environment to estimate targets. Results show that our method achieves satisfactory direction and reliable distance estimations, even in scenarios with low signal-to-noise ratios. Full article
(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)
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21 pages, 15470 KiB  
Article
Finite-Time Fault-Tolerant Tracking Control for an Air Cushion Vehicle Subject to Actuator Faults
by Renhai Yu, Qizheng Zhou and Tieshan Li
J. Mar. Sci. Eng. 2025, 13(2), 210; https://doi.org/10.3390/jmse13020210 - 22 Jan 2025
Viewed by 806
Abstract
This paper proposes a finite-time fault-tolerant tracking controller for an air cushion vehicle (ACV) based on the backstepping method. A four-degree-of-freedom ACV with model uncertainties is considered, where the unknown nonlinearities can be approximated by radial basis function neural networks. By combining the [...] Read more.
This paper proposes a finite-time fault-tolerant tracking controller for an air cushion vehicle (ACV) based on the backstepping method. A four-degree-of-freedom ACV with model uncertainties is considered, where the unknown nonlinearities can be approximated by radial basis function neural networks. By combining the command filter with the backstepping method, the calculation of virtual control derivatives is avoided. The proposed adaptive finite-time fault-tolerant controller can estimate the unknown boundaries of actuator fault parameters so that an unbounded number of actuator faults can be processed. The proposed theory ensures that the stability of the system and its tracking performance can be guaranteed in a finite time. This paper focuses on simulation-based work. Simulation results confirm the capability of the proposed trajectory tracking control scheme. Full article
(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)
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27 pages, 5999 KiB  
Article
Modeling and Analysis of Actuators in Multi-Pump Waterjet Propulsion Systems
by Shuli Jia, Yinuo Guo, Yuxue Liu, Dali Wei, Chong Qu and Liyong Ma
J. Mar. Sci. Eng. 2025, 13(1), 154; https://doi.org/10.3390/jmse13010154 - 17 Jan 2025
Viewed by 761
Abstract
Waterjet propulsion, which generates thrust by ejecting water jets, has attracted significant attention in modern high-performance vessels due to its efficiency, superior cavitation resistance, and excellent maneuverability. While previous research has primarily concentrated on optimizing the overall performance of waterjet propulsion systems, insufficient [...] Read more.
Waterjet propulsion, which generates thrust by ejecting water jets, has attracted significant attention in modern high-performance vessels due to its efficiency, superior cavitation resistance, and excellent maneuverability. While previous research has primarily concentrated on optimizing the overall performance of waterjet propulsion systems, insufficient attention has been paid to the detailed dynamic modeling of actuators in multi-pump systems, a critical component for improving system control precision. This paper addresses this gap by developing dynamic models for the reversing bucket and rudder angle actuators in marine waterjet propulsion systems. Based on an in-depth analysis of their working principles and operational parameters, transfer function models are established to simulate actuator performance under various conditions, including wear, hydraulic oil leakage, and external disturbances. Key influencing factors for each condition are identified, and corresponding parameter-setting models are constructed. The models’ response speed and steady-state accuracy are validated through step and ramp tests, confirming their effectiveness and reliability. The proposed model is verified with real measurement experiments and comparisons. The findings of this study contribute new insights into the dynamic behavior of multi-pump waterjet propulsion systems and provide a solid theoretical foundation for the future development of optimized control strategies in complex marine propulsion environments. Full article
(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)
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17 pages, 11245 KiB  
Article
Underwater Object Detection Algorithm Based on an Improved YOLOv8
by Fubin Zhang, Weiye Cao, Jian Gao, Shubing Liu, Chenyang Li, Kun Song and Hongwei Wang
J. Mar. Sci. Eng. 2024, 12(11), 1991; https://doi.org/10.3390/jmse12111991 - 5 Nov 2024
Cited by 5 | Viewed by 2019
Abstract
Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB [...] Read more.
Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB building blocks into the backbone network, along with the optimization of the C2f structure and the incorporation of large-kernel depthwise convolutions, effectively enhances the model’s receptive field. This improvement increases the capability of detecting multi-scale objects in complex underwater environments without adding a computational burden. Next, the incorporation of a Partial Self-Attention (PSA) module at the end of the backbone network enhances model efficiency and optimizes the utilization of computational resources while maintaining high performance. Finally, the integration of the Neck component from the Gold-YOLO model improves the neck structure of the YOLOv8 model, facilitating the fusion and distribution of information across different levels, thereby achieving more efficient information integration and interaction. Experimental results show that YOLOv8-CPG significantly outperforms the traditional YOLOv8 in underwater environments. Precision and Recall show improvements of 2.76% and 2.06%. Additionally, mAP50 and mAP50-95 metrics have increased by 1.05% and 3.55%, respectively. Our approach provides an efficient solution to the difficulties encountered in underwater object detection. Full article
(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)
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29 pages, 1721 KiB  
Article
Optimized Trajectory Tracking for ROVs Using DNN + ENMPC Strategy
by Guanghao Yang, Weidong Liu, Le Li, Jingming Xu, Liwei Guo and Kang Zhang
J. Mar. Sci. Eng. 2024, 12(10), 1827; https://doi.org/10.3390/jmse12101827 - 13 Oct 2024
Viewed by 1079
Abstract
This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed [...] Read more.
This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed double closed-loop control system operates in two distinct phases: (1) The outer loop controller uses a DNN controller to replace the LMPC controller, overcoming the uncertainties in the kinematic model while reducing the computational burden. (2) The inner loop velocity controller is designed using a nonlinear model predictive control (NMPC) algorithm with its closed-loop stability proven. A DNN + ENMPC 3D trajectory tracking method is proposed, integrating a velocity threshold-triggered mechanism into the inner-loop NMPC controller to reduce computational iterations while sacrificing only a small amount of tracking control performance. Finally, simulation results indicate that compared with the ENMPC algorithm, NMPC + ENMPC can better track the desired trajectory, reduce thruster oscillations, and further minimize the computational load. Full article
(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)
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