Zhang Boyu, Su Yishan, Sun Shanlin, Luo Wei, Huang Qing
School of Aeronautics and Astronautics, Guilin University of Aerospace Technology, Guilin, 541004, China.
School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
Sci Rep. 2025 Aug 5;15(1):28493. doi: 10.1038/s41598-025-11500-1.
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.
为满足自主水下航行器（AUV）的运动学约束和实时要求，本文提出了一种新颖的路径规划算法：方向锥与目标偏向动态人工势场RRT*（DCGB-DAPF-RRT*）。该算法集成了四项关键技术——方向锥采样、目标偏向采样、自适应步长和动态人工势场，以提高采样效率并改善基于传统RRT方法的路径质量。此外，采用冗余节点修剪和三次非均匀B样条插值来增强轨迹平滑度并确保运动学可行性。实验和仿真结果表明，所提算法将路径规划时间减少了50.0 - 73.6%，节点数量减少了71.0 - 77.8%，迭代次数降低了61.0 - 85.0%，同时在复杂环境中成功率达到100%。最终路径长度缩短至1766.1米，最大转弯角度限制在11.35°，完全满足AUV的运动约束。
