Underwater Vehicle Path Planning Based on the Improved Bidirectional APF-RRT* Algorithm

Aiming at the problems of traditional Rapidly exploring Random Tree Star (RRT*), its modified algorithms in the three-dimensional complex underwater path planning of autonomous underwater vehicles (AUVs), including sampling redundancy, simplistic expansion mechanism, mismatch between planned paths and AUV motion constraints, and insufficient real-time performance, this paper proposes an improved bidirectional artificial potential field RRT* (Improved BI-APF-RRT*) algorithm. The algorithm adopts a hybrid sampling strategy to concentrate sampling in high-value regions and optimize node distribution. A three-level progressive expansion strategy is designed to balance fast convergence and excellent obstacle avoidance capability. Meanwhile, a dynamic target switching strategy is introduced to enhance the coordination efficiency of bidirectional search trees. Simulation results show that in three-dimensional underwater obstacle environments with different complexity levels, the proposed algorithm outperforms comparative algorithms such as GB-RRT*, BI-RRT*, and APF-RRT* in terms of path length, planning time, number of generated nodes, and iteration times. The proposed algorithm provides an efficient and feasible technical scheme for the deep-sea autonomous navigation of AUVs, and is of great significance for promoting the engineering application of underwater unmanned systems.