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<p>Unmanned Underwater Vehicles (UUVs) are increasingly important in coastal ecological protection and marine resource utilization, where autonomous obstacle‐avoidance trajectory planning is essential to ensure reliable operation. To handle the complex and dynamic nearshore environments, this study develops an enhanced Dynamic Window Approach (DWA), incorporating fuzzy logic reasoning. A rapid angular velocity variation evaluation function is introduced to improve the smoothness of planned trajectories, while a safety coefficient combined with a fuzzy inference-based dynamic weight adjustment strategy enables adaptive parameter allocation in response to varying obstacle distributions. These improvements elevate the environmental adaptability of UUVs and reduce the occurrence of local-optimal solutions. Extensive MATLAB simulation trials conducted under diverse dynamic scenarios verify that the method provides strong reliability and performance stability. Furthermore, results obtained from realistic underwater experiments show that the refined algorithm yields superior obstacle avoidance success, longer safe-navigation distances, smoother planned paths, and improved energy utilization when compared with the conventional DWA framework.</p>

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Enhanced dynamic window approach for autonomous obstacle avoidance in unmanned underwater vehicles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

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  • Xiaojing Fan,
  • Fang Kong,
  • Xin’ao Xie,
  • Yinjing Guo

摘要

Unmanned Underwater Vehicles (UUVs) are increasingly important in coastal ecological protection and marine resource utilization, where autonomous obstacle‐avoidance trajectory planning is essential to ensure reliable operation. To handle the complex and dynamic nearshore environments, this study develops an enhanced Dynamic Window Approach (DWA), incorporating fuzzy logic reasoning. A rapid angular velocity variation evaluation function is introduced to improve the smoothness of planned trajectories, while a safety coefficient combined with a fuzzy inference-based dynamic weight adjustment strategy enables adaptive parameter allocation in response to varying obstacle distributions. These improvements elevate the environmental adaptability of UUVs and reduce the occurrence of local-optimal solutions. Extensive MATLAB simulation trials conducted under diverse dynamic scenarios verify that the method provides strong reliability and performance stability. Furthermore, results obtained from realistic underwater experiments show that the refined algorithm yields superior obstacle avoidance success, longer safe-navigation distances, smoother planned paths, and improved energy utilization when compared with the conventional DWA framework.