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Dynamics, Control, and Design of Bionic Underwater Vehicles
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Dynamics, Control, and Design of Bionic Underwater Vehicles

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: 1 December 2026 | Viewed by 1085

Special Issue Editors

Prof. Dr. Yilin Qu

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: underwater vehicle; bionic robotics; marine structures; materials modeling; structural analysis; electromechanical coupling phenomena with applications in smart structures
Prof. Dr. Hongsheng Dong

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: underwater energy systems; power and propulsion; energy storage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fuwang Zhao

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: fluid–structure interaction; active/passive control; underwater vehicle
Prof. Dr. Denghui Qin

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: fluid mechanics; acoustic signal processing; hydrodynamic/structural vibration/cavitation noise

Special Issue Information

Dear Colleagues,

Bionic underwater vehicles (BUVs), which draw inspiration from the morphological, locomotion, and sensory adaptations of aquatic organisms, represent a significant development in marine robotics. These systems offer the potential to achieve superior maneuverability, efficiency, and environmental compatibility compared to conventional designs, with applications spanning ecological monitoring to underwater infrastructure inspection. However, the development of performant and reliable BUVs is impeded by fundamental interdisciplinary challenges. The core difficulties lie in the tight coupling between bio-inspired mechanical design, the complex fluid–structure interactions governing their dynamics, and the synthesis of effective control strategies under significant hydrodynamic uncertainties. Progress in this field necessitates advances in high-fidelity modeling, robust control architectures, and the holistic integration of design and autonomy.

This Special Issue aims to present a collection of research that addresses these critical issues. We encourage contributions that advance the theoretical foundations, computational methods, and experimental validation of BUVs, pushing the boundaries of their capabilities and practical applicability. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Energy systems and harvesting technologies;
  • Bio-inspired propulsion mechanism design and modeling;
  • Novel actuation systems and smart material applications;
  • Nonlinear dynamics and fluid–structure interaction analysis;
  • Robust and adaptive control strategies;
  • Bio-inspired control architectures and central pattern generators;
  • Morphological design and multidisciplinary optimization;
  • Biomimetic sensing and perception systems;
  • Autonomous navigation and decision-making in uncertain environments;
  • Multi-vehicle cooperation and swarm intelligence.

We welcome the submission of original research articles and comprehensive reviews that offer novel insights and substantiated results. Contributions that provide open-source datasets, models, or benchmark problems for the community are particularly encouraged.

Prof. Dr. Yilin Qu
Prof. Dr. Hongsheng Dong
Prof. Dr. Fuwang Zhao
Prof. Dr. Denghui Qin
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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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

  • bionic underwater vehicles
  • bio-inspired propulsion
  • robust control
  • morphological design
  • fluid–structure interaction
  • autonomous underwater vehicles

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Published Papers (2 papers)

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Research

35 pages, 6664 KB  
Article
Dynamic Modeling and Integrated Optimization Design of a Biomimetic Skipping Plate for Hybrid Aquatic–Aerial Vehicle
by Fukui Gao, Wei Yang, Lei Yu, Zhe Zhang, Wenhua Wu and Xinlin Li
J. Mar. Sci. Eng. 2026, 14(8), 744; https://doi.org/10.3390/jmse14080744 - 18 Apr 2026
Viewed by 304
Abstract
A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV [...] Read more.
A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article
(This article belongs to the Special Issue Dynamics, Control, and Design of Bionic Underwater Vehicles)
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17 pages, 1657 KB  
Article
HDAO: A Hierarchical Curiosity-Driven Reinforcement Learning Approach for AUV Dynamic Obstacle Avoidance
by Huazheng Du, Qian Liu, Xu Liu and Na Xia
J. Mar. Sci. Eng. 2026, 14(8), 720; https://doi.org/10.3390/jmse14080720 - 14 Apr 2026
Viewed by 415
Abstract
Autonomous obstacle avoidance is a critical capability for Autonomous Underwater Vehicles (AUVs) to operate safely in dynamic and uncertain marine environments. Traditional AUV control methods rely on precise physical modeling and preset rules, yet they struggle to adapt to multiple sources of uncertainty, [...] Read more.
Autonomous obstacle avoidance is a critical capability for Autonomous Underwater Vehicles (AUVs) to operate safely in dynamic and uncertain marine environments. Traditional AUV control methods rely on precise physical modeling and preset rules, yet they struggle to adapt to multiple sources of uncertainty, such as random initial states, dynamic obstacles, and varying currents. In recent years, deep reinforcement learning has provided a new avenue for data-driven adaptive policy learning. However, it remains insufficient for handling long-horizon tasks with sparse rewards. While hierarchical reinforcement learning can mitigate reward sparsity through temporal abstraction, it often faces challenges including exploration–exploitation imbalance, slow global convergence, and insufficient safety guarantees. Furthermore, most existing studies neglect dynamic environmental disturbances and task continuity, which further limits the practical application of these algorithms. To address these challenges, this paper proposes a hierarchical curiosity-driven AUV obstacle avoidance algorithm (HDAO), designed for autonomous obstacle avoidance in dynamic and uncertain underwater environments. The core design of HDAO incorporates several key innovations. Firstly, it introduces a Collision Threat Index for dynamic obstacles, which enables explicit risk perception and quantifies collision threats, thereby enhancing the policy’s generalization and robustness. Secondly, a task-decoupled hierarchical architecture is employed to synergistically optimize global path planning and local obstacle avoidance behaviors. This approach effectively manages long-horizon navigation tasks while alleviating high-dimensional training pressure. Finally, a novel reward mechanism is designed by integrating hierarchical active exploration with curiosity-driven passive exploration. This mechanism effectively incentivizes the agent to explore unvisited areas under sparse reward conditions and dynamically balances exploration and exploitation. Experimental results demonstrate that HDAO significantly outperforms existing methods in terms of obstacle avoidance success rate, training convergence speed and robustness against external disturbances. Full article
(This article belongs to the Special Issue Dynamics, Control, and Design of Bionic Underwater Vehicles)
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