Research on optimal path sampling algorithm of manipulator based on potential function

Abstract

Aiming at the problems of low success rate, long time and tortuous path of the traditional Rapidly-exploring Random Trees series of algorithms for path planning, this paper proposes the optimal path sampling algorithm based on the potential function (AP-RRT*), which solves the path planning problem of the manipulator in three-dimensional space. First, the potential function is defined and the concept of sampling termination distance is proposed. Second, a secondary sampling strategy is proposed in combination with the potential function to improve the planning speed and increase the coverage rate. Third, adaptive weights and adaptive step size are used to dynamically adjust the planning direction and distance, thereby improving the planning efficiency. Moreover, when performing node reconnection, dynamic retrieval circles are set to ensure path quality while reducing computational consumption. Finally, the improved algorithm, along with other algorithms, is simulated and experimentally verified in MATLAB and ROS. The results show that the AP-RRT* algorithm is superior in terms of path length, planning time, iterations, number of waypoints and success rate.