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Drones
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Advances in Intelligent Coordination Control for Autonomous UUVs
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Advances in Intelligent Coordination Control for Autonomous UUVs

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Design and Development".

Deadline for manuscript submissions: 25 June 2026 | Viewed by 6599

Special Issue Editors

Prof. Dr. Haitao Zhang

E-Mail Website
Guest Editor
School of Artifical Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: AI; swarm ntelligence; collaborative manufacturing of swarm robots; autonomous unmanned vehicle
Dr. Bin-Bin Hu

E-Mail Website
Guest Editor
Discrete Technology & Production Automation, University of Groningen, 9747 AG Groningen, The Netherlands
Interests: control theory; multi-robot systems; coordinated navigation
Dr. Zhenghong Jin

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: source seeking; nonlinear system; distributed optimization and control
Special Issues, Collections and Topics in MDPI journals
Dr. Mingkang Long

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: multi-agent systems; Euler-Lagrange systems; coordination control; consensus; controllability; formal methods
Dr. Henglai Wei

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100083, China
Interests: intelligent unmanned system; decision and control; model predictive control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, intelligent coordination control for autonomous underwater unmanned vehicles (AUUVs) has garnered increasing attention due to their vast potential across various fields, including environmental surveillance, disaster monitoring, underwater exploration, logistics, and pipeline maintenance. These autonomous systems, often working cooperatively with multiple vehicles, require advanced control mechanisms to ensure efficient operation, broad coverage, and robust performance, particularly in dynamic and unpredictable environments. AUUVs must coordinate their motions in real time, frequently without human intervention, which demands sophisticated decision-making frameworks. The incorporation of advanced technologies like game theory, optimization algorithms, and machine learning has been pivotal in advancing these systems. Game theory enhances strategic interaction between vehicles, optimization techniques improve resource allocation and decision-making, and machine learning allows systems to learn and enhance performance from past experiences. Collectively, these technologies enable more robust, adaptive, and real-time control of AUUV operations. This research area is vital for the future of autonomous systems, addressing key challenges such as ensuring operational safety and optimizing system performance. These innovations will be instrumental in realizing the full potential of AUUVs across a wide range of industries.

This Special Issue aims to explore advanced coordination control strategies and intelligent decision-making mechanisms for AUUVs. The focus will be on leveraging machine learning, game theory, and optimization techniques to enable highly efficient and adaptive multi-agent coordination in dynamic underwater environments. Additionally, the issue will examine the interplay between model-based and data-driven approaches to enhance decision-making processes, facilitating real-time adaptability and system optimization. Lastly, the development of innovative control algorithms will be emphasized, ensuring improved operational safety, coordination precision, and intelligence in various applications, including environmental monitoring, logistics, and autonomous underwater exploration.

This Special Issue will welcome manuscripts that link the following themes:

  • Different Applications of the Coordination Control of AUUVs.
  • Intelligent Decision-Making, Planning, and Navigation Coordination Control of AUUVs.
  • Distributed Optimization and Data-Driven Coordination Control of the AUUVs.
  • Learning-Based Coordination Control of the AUUVs.
  • Heterogeneous and Cross-Domain Coordination Control of the AUUVs.
  • Anti-Disturbance and Resilient Coordination Control for AUUVs.
  • Fault-Tolerance Coordination Control and Application of AUUVs.
  • Safety-Based and Pravicy-Based Coordination Control of AUUVs.
  • System Identification and Model-Based Coordination Control of AUUVs.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Haitao Zhang
Dr. Bin-Bin Hu
Dr. Zhenghong Jin
Dr. Mingkang Long
Dr. Henglai Wei
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. Drones 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

  • cooperative control
  • autonomous underwater unmanned vehicles
  • decision making
  • game theory
  • machine learning
  • control theory
  • distributed optimization
  • intelligent connected vehicles
  • vehicle dynamics control

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Published Papers (5 papers)

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Research

21 pages, 7971 KB  
Article
Timescale-Separation-Based Source Seeking for USV
by Chenxi Gong, Hexuan Wang, Chongqing Chen and Zhenghong Jin
Drones 2025, 9(12), 879; https://doi.org/10.3390/drones9120879 - 18 Dec 2025
Abstract
The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a [...] Read more.
The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a hierarchical control framework that divides the closed-loop system into a slow and a fast subsystem. The slow subsystem governs the gradual evolution of the USV pose and generates reference heading and surge commands from local scalar field information, providing a directional cue toward the field extremum. The fast subsystem applies actuator-level control inputs that ensure these references are tracked with sufficient accuracy through rapid corrective actions. A Lyapunov-based analysis is carried out to study the stability properties of the coupled slow–fast dynamics and to establish conditions under which convergence can be guaranteed in the presence of model nonlinearities and external disturbances. Numerical simulations are conducted to illustrate the resulting system behavior and to verify that the proposed framework maintains stable seeking performance under typical operating conditions. Full article
(This article belongs to the Special Issue Advances in Intelligent Coordination Control for Autonomous UUVs)
22 pages, 971 KB  
Article
Joint Path Planning and Energy Replenishment Optimization for Maritime USV–UAV Collaboration Under BeiDou High-Precision Navigation
by Jingfeng Yang, Lingling Zhao and Bo Peng
Drones 2025, 9(11), 746; https://doi.org/10.3390/drones9110746 - 28 Oct 2025
Viewed by 685
Abstract
With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high [...] Read more.
With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article
(This article belongs to the Special Issue Advances in Intelligent Coordination Control for Autonomous UUVs)
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17 pages, 3584 KB  
Article
Task Allocation and Path Planning Method for Unmanned Underwater Vehicles
by Feng Liu, Wei Xu, Zhiwen Feng, Changdong Yu, Xiao Liang, Qun Su and Jian Gao
Drones 2025, 9(6), 411; https://doi.org/10.3390/drones9060411 - 6 Jun 2025
Cited by 3 | Viewed by 1056
Abstract
Cooperative operations of Unmanned Underwater Vehicles (UUVs) have extensive applications in fields such as marine exploration, ecological observation, and subsea security. Path planning, as a key technology for UUV autonomous navigation, is crucial for enhancing the adaptability and mission execution efficiency of UUVs [...] Read more.
Cooperative operations of Unmanned Underwater Vehicles (UUVs) have extensive applications in fields such as marine exploration, ecological observation, and subsea security. Path planning, as a key technology for UUV autonomous navigation, is crucial for enhancing the adaptability and mission execution efficiency of UUVs in complicated marine environments. However, existing methods still have significant room for improvement in handling obstacles, multi-task coordination, and other complex problems. In order to overcome these issues, we put forward a task allocation and path planning method for UUVs. First, we introduce a task allocation mechanism based on an Improved Grey Wolf Algorithm (IGWA). This mechanism comprehensively considers factors such as target value, distance, and UUV capability constraints to achieve efficient and reasonable task allocation among UUVs. To enhance the search efficiency and accuracy of task allocation, a Circle chaotic mapping strategy is incorporated into the traditional GWA to improve population diversity. Additionally, a differential evolution mechanism is integrated to enhance local search capabilities, effectively mitigating premature convergence issues. Second, an improved RRT* algorithm termed GR-RRT* is employed for UUV path planning. By designing a guidance strategy, the sampling probability near target points follows a two-dimensional Gaussian distribution, ensuring obstacle avoidance safety while reducing redundant sampling and improving planning efficiency. Experimental results demonstrate that the proposed task allocation mechanism and improved path planning algorithm exhibit significant advantages in task completion rate and path optimization efficiency. Full article
(This article belongs to the Special Issue Advances in Intelligent Coordination Control for Autonomous UUVs)
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19 pages, 7302 KB  
Article
Safe and Optimal Motion Planning for Autonomous Underwater Vehicles: A Robust Model Predictive Control Framework Integrating Fast Marching Time Objectives and Adaptive Control Barrier Functions
by Zhonghe Tian and Mingzhi Chen
Drones 2025, 9(4), 273; https://doi.org/10.3390/drones9040273 - 3 Apr 2025
Viewed by 1609
Abstract
Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods [...] Read more.
Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods oversimplify obstacle geometries, and both fail to ensure safety and satisfy turning radius constraints for under-actuated AUVs in intricate environments. This paper proposes a robust Model Predictive Control (MPC) framework integrating an enhanced fast marching control barrier function, specifically designed for AUVs equipped with fully directional sonar systems. The framework introduces a novel improvement for moving obstacles by extending the control barrier function field propagation along the obstacle’s movement direction. This enhancement generates precise motion plans that ensure safety, satisfy kinematic constraints, and effectively handle static and dynamic obstacles. Simulation results demonstrate superior obstacle avoidance and motion planning performance in complex scenarios, with key outcomes including a minimum safety margin of 1.86 m in cluttered environments (vs. 0 m for A* and FMM) and 1.76 m in dynamic obstacle scenarios (vs. 0.13 m for MPC-APFCBF), highlighting the framework’s ability to enhance navigation safety and efficiency for real-world AUV deployments in unpredictable marine environments. Full article
(This article belongs to the Special Issue Advances in Intelligent Coordination Control for Autonomous UUVs)
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21 pages, 3441 KB  
Article
Task Allocation and Saturation Attack Approach for Unmanned Underwater Vehicles
by Qiangqiang Chen, Baisheng Liu, Changdong Yu, Mingkai Yang and Haonan Guo
Drones 2025, 9(2), 115; https://doi.org/10.3390/drones9020115 - 4 Feb 2025
Cited by 1 | Viewed by 2230
Abstract
In modern marine warfare, unmanned underwater vehicles (UUVs) have fast and efficient attack capabilities. However, existing research on UUV attack strategies is relatively limited, often ignoring the requirement for the effective allocation of different strategic value areas, which restricts its performance in the [...] Read more.
In modern marine warfare, unmanned underwater vehicles (UUVs) have fast and efficient attack capabilities. However, existing research on UUV attack strategies is relatively limited, often ignoring the requirement for the effective allocation of different strategic value areas, which restricts its performance in the marine combat environment. To this end, this paper proposes an innovative UUV task allocation and saturation attack strategy. The strategy first divides the area according to the distribution density of enemy UUVs, and then reasonably allocates tasks according to the enemy’s regional value and the attack capability of our UUVs. Our UUVs then sail to the enemy area and are evenly distributed in the encirclement to ensure accurate saturation attacks. In the task allocation link, the grey wolf optimizer is improved by introducing Logistic chaos mapping and differential evolution mechanism, which improves the search efficiency and allocation accuracy. At the same time, the combination of the optimal matching algorithm and Bezier curve dynamic path control ensures the accuracy and flexibility of a coordinated attack. The simulation experimental results show that the strategy shows high attack efficiency and practicality in marine combat scenarios, providing an effective solution for UUV attack tasks in complex marine environments. Full article
(This article belongs to the Special Issue Advances in Intelligent Coordination Control for Autonomous UUVs)
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