Collaborative Motion Planning for Dual Robotic Arms Based on Split Sampling Space RRT

Abstract

Aiming at the problems of strong sampling randomness, difficulty in collision detection and rough paths of rapidly exploring random tree (RRT) algorithm in collaborative motion planning of dual robotic arms, we propose an RRT algorithm based on split sampling space. First, a split sampling space strategy is proposed. According to the sampling points having a fixed range in a certain axis degree, the step size of random tree generation is restricted to the respective sampling space, and combined with the hierarchical wraparound box method to achieve effective collision detection. Besides, a greedy strategy is used to speed up the growth of random trees in the respective space. Finally, the trajectory smoothing of the dual robotic arms path using the Bezier curve improves the trajectory quality while ensuring that the dual robotic arms will not collide. The feasibility and effectiveness of the algorithm are verified through simulation experiments as well as real UR experiments.