An Improved Potential Bi-Directional RRT Path Planning Method for Space Redundant Robots
Weiran He, Xinle Yan, Haozhe Liu, Lingling Shi · 2024
RRT-Connect method can be utilized to find feasible paths for space redundant robots when performing complex in-orbit operations. However, its reliance on random sampling without leveraging obstacle information often leads to the generation of numerous invalid nodes that collide with obstacles, thereby increasing computational costs. To address these limitations, this study proposes an Improved Potential Bi-Directional RRT (IPB-RRT) algorithm that integrates an enhanced potential field and a pre-sampling strategy. The improved potential field tackles the local minima problem by refining the repulsive force and incorporating an RRT-exchange mechanism based on tree structure information. The pre-sampling strategy gathers prior environmental information by identifying collision states in joint space before tree expansion begins, enabling more efficient pathfinding. Simulation results in both simple and complex environments demonstrate that the proposed IPB-RRT algorithm significantly outperforms RRT-Connect by achieving shorter planning times, greater obstacle distance, and a substantially reduced number of invalid nodes. These improvements enhance the safety and reliability of paths, making the algorithm suitable for space robots performing challenging tasks.