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<p>To address the kinematic constraints and real-time requirements of autonomous underwater vehicle (AUV), this paper proposes a novel path planning algorithm: Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*). The algorithm integrates four key techniques-Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and a Dynamic Artificial Potential Field-to improve sampling efficiency and path quality over conventional RRT-based methods. In addition, redundant node pruning and cubic non-uniform B-spline interpolation are employed to enhance trajectory smoothness and ensure kinematic feasibility. Experimental and simulation results demonstrate that the proposed algorithm reduces path planning time by 50.0–73.6%, decreases the number of nodes by 71.0–77.8%, and lowers the number of iterations by 61.0–85.0%, while achieving a 100% success rate in complex environments. The final path length is reduced to 1766.1 m, and the maximum turning angle is limited to 11.35°, fully satisfying the motion constraints of AUV.</p>

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Hybrid path planning algorithm for underactuated AUV based on RRT star and APF

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月5日

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  • Boyu Zhang,
  • Yishan Su,
  • Shanlin Sun,
  • Wei Luo,
  • Qing Huang

摘要

To address the kinematic constraints and real-time requirements of autonomous underwater vehicle (AUV), this paper proposes a novel path planning algorithm: Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*). The algorithm integrates four key techniques-Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and a Dynamic Artificial Potential Field-to improve sampling efficiency and path quality over conventional RRT-based methods. In addition, redundant node pruning and cubic non-uniform B-spline interpolation are employed to enhance trajectory smoothness and ensure kinematic feasibility. Experimental and simulation results demonstrate that the proposed algorithm reduces path planning time by 50.0–73.6%, decreases the number of nodes by 71.0–77.8%, and lowers the number of iterations by 61.0–85.0%, while achieving a 100% success rate in complex environments. The final path length is reduced to 1766.1 m, and the maximum turning angle is limited to 11.35°, fully satisfying the motion constraints of AUV.