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<p>Efficient multi-USV formation control in obstacle-rich maritime environments remains challenging, primarily due to local minima, goal non-reachability, and delayed response to dynamic obstacles in conventional path planning methods. This paper presents an enhanced Artificial Potential Field (APF) algorithm that directly addresses these three limitations for multi-USV formation and obstacle avoidance. This paper presents an enhanced Artificial Potential Field (APF) algorithm for multi-USV formation and obstacle avoidance, addressing issues such as local minima and goal non-reachability. The proposed method introduces improvements such as instantaneous escape forces, rotational factors, and relative velocity terms to the APF framework, enabling follower USVs to generate collision-free formation trajectories while improving obstacle avoidance efficiency in dynamic environments. The algorithm is designed to ensure smooth formation maintenance and effective obstacle avoidance, even in the presence of dynamic obstacles. Numerical simulations demonstrate that the proposed approach significantly enhances the performance of USV formations compared to traditional and existing APF-based methods, reducing target arrival times by 34.52% and 25.13%, respectively. The experimental results validate the practical applicability of the method, highlighting its ability to maintain USV formation integrity and avoid collisions in both static and dynamic obstacle scenarios. This work offers a promising solution for autonomous maritime operations.</p>

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Maritime obstacle-aware formation control for USVs with an optimized artificial potential field

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Zhicheng Tan,
  • Shanlin Sun,
  • Guo Li,
  • Liangpo Hong,
  • Haixin Sun

摘要

Efficient multi-USV formation control in obstacle-rich maritime environments remains challenging, primarily due to local minima, goal non-reachability, and delayed response to dynamic obstacles in conventional path planning methods. This paper presents an enhanced Artificial Potential Field (APF) algorithm that directly addresses these three limitations for multi-USV formation and obstacle avoidance. This paper presents an enhanced Artificial Potential Field (APF) algorithm for multi-USV formation and obstacle avoidance, addressing issues such as local minima and goal non-reachability. The proposed method introduces improvements such as instantaneous escape forces, rotational factors, and relative velocity terms to the APF framework, enabling follower USVs to generate collision-free formation trajectories while improving obstacle avoidance efficiency in dynamic environments. The algorithm is designed to ensure smooth formation maintenance and effective obstacle avoidance, even in the presence of dynamic obstacles. Numerical simulations demonstrate that the proposed approach significantly enhances the performance of USV formations compared to traditional and existing APF-based methods, reducing target arrival times by 34.52% and 25.13%, respectively. The experimental results validate the practical applicability of the method, highlighting its ability to maintain USV formation integrity and avoid collisions in both static and dynamic obstacle scenarios. This work offers a promising solution for autonomous maritime operations.