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

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.