Journal of Marine Science and Engineering

Research

20 pages, 6570 KiB

Open AccessArticle

Autonomous Vehicle Maneuvering Using Vision–LLM Models for Marine Surface Vehicles

by Tae-Yeon Kim and Woen-Sug Choi

J. Mar. Sci. Eng. 2025, 13(8), 1553; https://doi.org/10.3390/jmse13081553 - 13 Aug 2025

Recent advances in vision–language models (VLMs) have transformed the field of robotics. Researchers are combining the reasoning capabilities of large language models (LLMs) with the visual information processing capabilities of VLMs in various domains. However, most efforts have focused on terrestrial robots and [...] Read more.

Recent advances in vision–language models (VLMs) have transformed the field of robotics. Researchers are combining the reasoning capabilities of large language models (LLMs) with the visual information processing capabilities of VLMs in various domains. However, most efforts have focused on terrestrial robots and are limited in their applicability to volatile environments such as ocean surfaces and underwater environments, where real-time judgment is required. We propose a system integrating the cognition, decision making, path planning, and control of autonomous marine surface vehicles in the ROS2–Gazebo simulation environment using a multimodal vision–LLM system with zero-shot prompting for real-time adaptability. In 30 experiments, adding the path plan mode feature increased the success rate from 23% to 73%. The average distance increased from 39 m to 45 m, and the time required to complete the task increased from 483 s to 672 s. These results demonstrate the trade-off between improved reliability and reduced efficiency. Experiments were conducted to verify the effectiveness of the proposed system and evaluate its performance with and without adding a path-planning step. The final algorithm with the path-planning sub-process yields a higher success rate, and better average path length and time. We achieve real-time environmental adaptability and performance improvement through prompt engineering and the addition of a path-planning sub-process in a limited structure, where the LLM state is initialized with every application programming interface call (zero-shot prompting). Additionally, the developed system is independent of the vision–LLM archetype, making it scalable and adaptable to future models. Full article

(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)

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31 pages, 2334 KiB

Open AccessArticle

Weak Fault Feature Extraction for AUV Thrusters with Multi-Input Signals

by Dacheng Yu, Feng Yao, Yan Gao, Xing Liu and Mingjun Zhang

J. Mar. Sci. Eng. 2025, 13(8), 1519; https://doi.org/10.3390/jmse13081519 - 7 Aug 2025

Viewed by 115

Abstract

This paper investigates weak fault feature extraction in AUV thrusters under multi-input signal conditions. Conventional methods often rely on insufficient input signals, leading to a non-monotonic mapping between fault features and fault severity. This, in turn, makes accurate fault severity identification infeasible. To [...] Read more.

This paper investigates weak fault feature extraction in AUV thrusters under multi-input signal conditions. Conventional methods often rely on insufficient input signals, leading to a non-monotonic mapping between fault features and fault severity. This, in turn, makes accurate fault severity identification infeasible. To overcome this limitation, this paper increases the number of input signals by utilizing all available measurable signals. To address the problems arising from the expanded signal set, a signal denoising method that combines Feature Mode Decomposition and wavelet denoising is proposed. Furthermore, a signal enhancement technique that integrates energy operators and the Modified Bayes method. Additionally, distinct technical approaches for noise reduction and enhancement are specifically designed for different input signals. Unlike conventional methods that extract features directly from raw input signals, for fault feature extraction and fusion, this study transforms the signals into the time, frequency, and time–frequency domains, extracting diverse fault features across these domains. A sensitivity factor selection method is then employed to identify the sensitive features. These selected features are subsequently fused using Dempster–Shafer evidence theory to construct the final fault feature. Finally, fault severity identification is carried out using the classical grey relational analysis. Pool experiments using the “Beaver II” AUV prototype validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)

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23 pages, 15163 KiB

Open AccessArticle

3D Dubins Curve-Based Path Planning for UUV in Unknown Environments Using an Improved RRT* Algorithm

by Feng Pan, Peng Cui, Bo Cui, Weisheng Yan and Shouxu Zhang

J. Mar. Sci. Eng. 2025, 13(7), 1354; https://doi.org/10.3390/jmse13071354 - 16 Jul 2025

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Abstract

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved [...] Read more.

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)

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17 pages, 4941 KiB

Open AccessArticle

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 971

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

Open AccessArticle

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 911

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

Open AccessArticle

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 1041

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

Open AccessArticle

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 9 | Viewed by 2756

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

Open AccessArticle

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 1240

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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