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Design and Application of Underwater Vehicles

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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: closed (10 February 2026) | Viewed by 17023

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Prof. Dr. Zhiguang Feng

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College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Interests: intelligent control; underwater vehicles; neural network
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Dear Colleagues,

Underwater vehicles are part of the core technical equipment used for exploring and developing marine resources. Their designs and applications have irreplaceable strategic value in deep sea exploration, environmental monitoring, seabed resource surveying, and military security. With the continuous growth in the global demand for marine resources, underwater vehicles can not only perform high-precision seabed topography mapping and biodiversity surveys and other scientific tasks, but also be applied in the maintenance of seabed infrastructure (such as oil and gas pipelines and optical cable inspection) and marine disaster early warning scenarios, as well as other practical scenarios. Facing complex and changeable marine environments (such as high pressure, strong turbulence, and extreme temperature gradients), the design of underwater vehicles needs to balance energy efficiency and system reliability and use intelligent control algorithms (such as model predictive control and adaptive path planning) to deal with dynamic disturbances, ensuring the stability and safety of task execution. In addition, the autonomous operation ability of underwater vehicles under extreme conditions provides key technical support for advancing marine scientific research and environmental protection.

The research and development of underwater vehicles reflects the achievements of multi-disciplinary integration, covering mechanical structure optimization, intelligent perception system integration, and energy management strategies. Their design needs to address core issues such as multi-degree-of-freedom motion control, improvements in long-endurance capabilities, and the optimization of environmental adaptability, such as by using lightweight materials and modular architectures to reduce energy consumption and also by integrating data-driven real-time decision-making systems to enhance their obstacle avoidance and trajectory-tracking capabilities. High-precision numerical modeling and simulation technologies have further improved their operational accuracy in non-ideal environments, such as complex ocean currents and sensor noise. The combination of distributed sensor networks and artificial intelligence algorithms has also promoted the realization of fully autonomous and intelligent underwater operations. These technological innovations not only accelerate the progress of marine science and engineering, but also lay a solid foundation for future deep sea resource development, ecological protection, and global ocean governance.

Prof. Dr. Zhiguang Feng
Guest Editor

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Keywords

underwater vehicles
artificial intelligence
autonomous underwater vehicles
deep sea exploration

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Related Special Issue

Design and Application of Underwater Vehicles—2nd Edition in Journal of Marine Science and Engineering

Published Papers (11 papers)

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Research

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26 pages, 4597 KB

Open AccessArticle

Design and Motion Performance of an Underwater Two-Stage Towed System with Active Heave Compensation

by Zhan Wang, Pengfei Xu, Lei Yang, Meijie Cao and Hailong Lin

J. Mar. Sci. Eng. 2026, 14(10), 901; https://doi.org/10.3390/jmse14100901 (registering DOI) - 13 May 2026

Abstract

Underwater towed survey systems are widely used for marine observation, resource exploration, and target identification. While high-speed towing is increasingly required to improve operational efficiency, conventional single-stage towed systems face a critical trade-off: active heave compensation systems are complex and costly, whereas purely passive configurations lack sufficient disturbance rejection at higher speeds. To address this gap, this study proposes a two-stage towing system consisting of a vessel, heavy cable, depressor, light cable, and detection towed body, where the depressor functions as a mechanical low-pass filter. The depressor reduces vessel-induced heave motion transmission by approximately 79% compared with a conventional single-stage system. CFD simulations are conducted to evaluate hydrodynamic performance and extract key coefficients. A lumped-mass dynamic model is established for time-domain motion simulations. An integral sliding-mode controller with vessel heave feedforward compensation is designed to enhance depth-tracking capability. The active controller eliminates step response overshoot and provides robust depth regulation under wave disturbances. Sea trials under real ocean conditions validate the system’s motion stability, demonstrating satisfactory depth-keeping performance at high towing speeds. The simulation results show good agreement with experimental data, confirming the effectiveness of the proposed system and dynamic model. This work offers a practically validated towing platform solution for high-precision underwater survey operations. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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36 pages, 47250 KB

Open AccessArticle

PIRATE—Precision Imaging Real-Time Autonomous Tracker & Explorer

by Dan Zlotnikov and Ohad Ben-Shahar

J. Mar. Sci. Eng. 2026, 14(6), 558; https://doi.org/10.3390/jmse14060558 - 17 Mar 2026

Viewed by 493

Abstract

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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22 pages, 3630 KB

Open AccessArticle

Finite-Time Event-Triggered Formation Tracking Control of USVs Subject to Input Saturation Based on Active Disturbance Rejection Control

by Dongling Yu and Zhiguang Feng

J. Mar. Sci. Eng. 2026, 14(4), 394; https://doi.org/10.3390/jmse14040394 - 21 Feb 2026

Viewed by 536

Abstract

This paper proposes an integrated finite-time relative-threshold event-triggered control (FTRTETC) framework for unmanned surface vehicle (USV) formations under input saturation and unknown time-varying external disturbances. Firstly, a scheme of USV formation control based on signed graph theory is proposed. Next, a Gaussian error function is used to handle input saturation and simplify the backstepping design. Then, a finite-time formation controller is developed based on the active disturbance rejection control (ADRC) method with extended state observers (ESOs) and tracking differentiators (TDs). Also, a relative-threshold event-triggered mechanism is designed to reduce the frequency of control execution and communication load. By Lyapunov’s stability theory, the proposed controller is proven to achieve finite-time convergence, ensuring all closed-loop signals achieve global uniform ultimate boundedness (GUUB) and the system is without Zeno behaviour. Finally, numerical simulation examples are presented to validate the effectiveness and robustness of the proposed controller. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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34 pages, 6823 KB

Open AccessArticle

Three-Dimensional Autonomous Navigation of Unmanned Underwater Vehicle Based on Deep Reinforcement Learning and Adaptive Line-of-Sight Guidance

by Jianya Yuan, Hongjian Wang, Bo Zhong, Chengfeng Li, Yutong Huang and Shaozheng Song

J. Mar. Sci. Eng. 2025, 13(12), 2360; https://doi.org/10.3390/jmse13122360 - 11 Dec 2025

Cited by 1 | Viewed by 783

Abstract

Unmanned underwater vehicles (UUVs) face significant challenges in achieving safe and efficient autonomous navigation in complex marine environments due to uncertain perception, dynamic obstacles, and nonlinear coupled motion control. This study proposes a hierarchical autonomous navigation framework that integrates improved particle swarm optimization (PSO) for 3D global route planning, and a deep deterministic policy gradient (DDPG) algorithm enhanced by noisy networks and proportional prioritized experience replay (PPER) for local collision avoidance. To address dynamic sideslip and current-induced deviations during execution, a novel 3D adaptive line-of-sight (ALOS) guidance method is developed, which decouples nonlinear motion in horizontal and vertical planes and ensures robust tracking. The global planner incorporates a multi-objective cost function that considers yaw and pitch adjustments, while the improved PSO employs nonlinearly synchronized adaptive weights to enhance convergence and avoid local minima. For local avoidance, the proposed DDPG framework incorporates a memory-enhanced state–action representation, GRU-based temporal processing, and stratified sample replay to enhance learning stability and exploration. Simulation results indicate that the proposed method reduces route length by 5.96% and planning time by 82.9% compared to baseline algorithms in dynamic scenarios, it achieves an up to 11% higher success rate and 10% better efficiency than SAC and standard DDPG. The 3D ALOS controller outperforms existing guidance strategies under time-varying currents, ensuring smoother tracking and reduced actuator effort. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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29 pages, 5526 KB

Open AccessArticle

Design of UUV Underwater Autonomous Recovery System and Controller Based on Mooring-Type Mobile Docking Station

by Peiyu Han, Wei Zhang, Qiyang Wu and Yefan Shi

J. Mar. Sci. Eng. 2025, 13(10), 1861; https://doi.org/10.3390/jmse13101861 - 26 Sep 2025

Cited by 1 | Viewed by 1574

Abstract

This study addresses autonomous underwater vehicle (UUV) recovery onto dynamic docking stations by proposing a fork-column recovery control system with a segmented docking strategy (long-distance approach + guided descent). To enhance model fidelity, transmission lag of actuators is captured by a specified transfer function, and nonlinear dynamics are characterized as an improved quasi-linear parameter-varying (QLPV) model. An adaptive variable–prediction–step mechanism was designed to accommodate different phases of acoustic–optical guided recovery. A model predictive controller (MPC) was developed based on an improved dynamic model to effectively handle complex constraints during the recovery process. Simulation and physical experiments demonstrated that the proposed system significantly reduces errors, among which the control accuracy (tracking error under disturbance < 0.3 m) and docking success rate (>95%) are notably superior to traditional methods, providing a reliable solution for the dynamic recovery of unmanned underwater vehicles (UUVs). Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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26 pages, 101982 KB

Open AccessArticle

Hydrodynamic Optimization and Motion Stability Enhancement of Underwater Glider Combining CFD and MOPSO

by Tian Zhang, Jiaming Wu, Xianyuan Yang and Xiaodong Chen

J. Mar. Sci. Eng. 2025, 13(9), 1749; https://doi.org/10.3390/jmse13091749 - 10 Sep 2025

Viewed by 1276

Abstract

This study investigated the motion stability of underwater gliders and optimized their shape to enhance hydrodynamic performance. Given the critical role of stability in underwater operations, a multi-objective optimization framework was developed, focusing on the geometric configuration of hydrofoils. Computational fluid dynamics (CFD) simulations were employed, with stability assessed based on hydrodynamic moments in roll and pitch motions. A surrogate model was constructed using Kriging interpolation, leveraging Latin hypercube sampling (LHS) to generate 60 design points. Sensitivity analysis identified key shape parameters influencing stability, guiding a multi-objective particle swarm optimization (MOPSO) algorithm to explore optimal design configurations. Improvements of up to 68.91% in roll stability and 51.63% in pitch stability are achieved compared to the original model, which demonstrates the effectiveness of the proposed optimization approach. The findings provide valuable insights into the hydrodynamic design of underwater gliders, facilitating enhanced maneuverability and stability in complex marine environments. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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22 pages, 10200 KB

Open AccessArticle

Research on Self-Noise Processing of Unmanned Surface Vehicles via DD-YOLO Recognition and Optimized Time-Frequency Denoising

by Zhichao Lv, Gang Wang, Huming Li, Xiangyu Wang, Fei Yu, Guoli Song and Qing Lan

J. Mar. Sci. Eng. 2025, 13(9), 1710; https://doi.org/10.3390/jmse13091710 - 4 Sep 2025

Viewed by 1382

Abstract

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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23 pages, 12274 KB

Open AccessArticle

Predefined-Time Formation Tracking Control for Underactuated AUVs with Input Saturation and Output Constraints

by Sibo Yao, Yiqi Wang and Zhiguang Feng

J. Mar. Sci. Eng. 2025, 13(9), 1607; https://doi.org/10.3390/jmse13091607 - 22 Aug 2025

Cited by 1 | Viewed by 1206

Abstract

In this work, a predefined-time formation output constraint control method is proposed for underactuated AUVs with input saturation. First, a coordinate transformation method is utilized to convert the underactuated AUV system into a fully actuated system form. A universal time-varying asymmetric barrier function is constructed to convert the system to an unconstrained form and construct the formation tracking error. Then, a predefined-time formation output constraint control law is designed based on the active disturbance rejection control framework and predefined-time control method, which can achieve the control objective without relying on the precise mathematical model of the system. In addition, to address the input saturation issue, a novel predefined-time auxiliary dynamic system (ADS) is proposed. The proposed method with ADS can ensure that the multi-AUV system with input saturation can complete the formation output constraint tracking control task within a predefined time. Finally, a simulation is designed to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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20 pages, 2749 KB

Open AccessArticle

ROVs Utilized in Communication and Remote Control Integration Technologies for Smart Ocean Aquaculture Monitoring Systems

by Yen-Hsiang Liao, Chao-Feng Shih, Jia-Jhen Wu, Yu-Xiang Wu, Chun-Hsiang Yang and Chung-Cheng Chang

J. Mar. Sci. Eng. 2025, 13(7), 1225; https://doi.org/10.3390/jmse13071225 - 25 Jun 2025

Cited by 7 | Viewed by 3102

Abstract

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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23 pages, 2564 KB

Open AccessArticle

Hierarchical Adaptive Fixed-Time Formation Control for Multiple Underactuated Autonomous Underwater Vehicles Under Uncertain Disturbances and Input Saturation

by Jiacheng Chang, Lanyong Zhang, Yifan Tan, Xue Fu and Hongjun Yu

J. Mar. Sci. Eng. 2025, 13(6), 1146; https://doi.org/10.3390/jmse13061146 - 9 Jun 2025

Cited by 1 | Viewed by 1542

Abstract

Recent advances in multiple autonomous underwater vehicles (AUVs) have highlighted formation control as a critical challenge for underwater collaborative operations. To address the inherent coupling between formation coordination and individual control in conventional approaches, this paper proposes a novel hierarchical framework of adaptive fixed-time formation control for multiple underactuated AUVs. This framework decouples AUVs’ formation requirements and individual control challenges into two distinct layers: the Collision-free Formation Trajectories Generation (CFTG) Layer and the Adaptive Trajectories Tracking (ATT) Layer. In the CFTG Layer, a consensus-based controller is developed to generate the desired trajectories for the AUVs to meet the requirements of complex formation tasks. And an improved artificial potential field method is proposed to ensure AUVs can reach the target point when the target is close to obstacles. In the ATT Layer, an auxiliary compensation system is designed to address the issue of input saturation. Furthermore, the adaptive fixed-time controllers are proposed to handle the uncertain parameters in the model, enabling underactuated AUVs to track the desired trajectory precisely. Both layers guarantee fixed-time convergence to increase the convergence speed. Simulations are conducted to demonstrate the effectiveness and better performance of the proposed method. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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Review

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26 pages, 4820 KB

Open AccessReview

Variable-Stiffness Underwater Robotic Systems: A Review

by Peiwen Lu, Busheng Dong, Xiang Gao, Fujian Zhang, Yunyun Song, Zhen Liu and Zhongqiang Zhang

J. Mar. Sci. Eng. 2025, 13(9), 1805; https://doi.org/10.3390/jmse13091805 - 18 Sep 2025

Cited by 1 | Viewed by 4037

Abstract

Oceans, which cover more than 70% of Earth’s surface, are home to vast biological and mineral resources. Deep-sea exploration encounters significant challenges due to harsh environmental factors, including low temperatures, high pressure, and complex hydrodynamic forces. These constraints have led to the widespread use of underwater robots as essential tools for deep-sea resource exploration and exploitation. Conventional underwater robots, whether rigid with fixed stiffness or fully flexible, fail to achieve the propulsion efficiency observed in biological fish. To overcome this limitation, researchers have developed adjustable stiffness mechanisms for robotic fish designs. This innovation strikes a balance between structural rigidity for stability and flexible adaptability to dynamic environments. By dynamically adjusting localized stiffness, these bio-inspired robots can alter their mechanical properties in real time. This capability improves propulsion efficiency, energy utilization, and resilience to external disturbances during operation. This paper begins by reviewing the evolution of underwater robots, from fixed-stiffness systems to adjustable-stiffness designs. Next, existing methods for stiffness adjustment are categorized into two approaches: offline component replacement and online real-time adaptation. The principles, implementation strategies, and comparative advantages of each approach are then analyzed. Finally, we identify the current challenges in adjustable-stiffness underwater robotics and propose future directions, such as advancements in intelligent sensing, autonomous stiffness adaptation, and enhanced performance in extreme environments. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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