Robotic Arm Motion Planning Based on Constrained Sampling RRT

Abstract

Aiming at the problems of Rapidly-exploring Random Tree (RRT) for manipulator motion planning such as low nodes utilize efficiency,poor expansion guidance and poor path quality,a constrained sampling method based RRT algorithm was proposed.The sparse node generation mechanism was improved,which significantly enhanced the global search efficiency of RRT through reducing the repetitive sampling.Besides,a dynamic sampling region strategy was proposed to dynamically adjust the size of the sampling region and improve certainty of growth direction,so as to reduce the useless nodes.The greedy strategy was applied to improve the utilization rate of nodes and reduce iterations.After searching DSSP-RRT,β-spline curve was used to smooth the trajectory and improve its quality.Through the obstacle-avoidancing simulation under different situations and real UR5 experiment,the effectiveness of the proposed algorithm was proved.