An improved Rapidly-exploring Random Tree Approach for Robotic Dynamic Path Planning

Abstract

Aiming at solving the issue that the existing Rapidly-exploring Random Tree (RRT) algorithm cannot well replan the paths to avoid dynamic obstacles for robotic manipulator autonomously and rapidly in complex cluttered environments, three-dimensional reconstruction of the global dynamic scene around the robotic manipulator is carried out based on RGB-D visual sensor in this paper. A Bi-RRT-Star dynamic path planning approach based on improved exploring function with goal direction is proposed, which is improved from connection strategy, heuristic intensive exploring, and adjacent nodes expansion. On this basis, a multi-step expansion strategy with heuristic greedy is presented. Finally, the relevant evaluation indices of the proposed approach are verified in Virtual Robot Environment Platform (VREP) software. The simulation results show that in comparison with Bi-RRT and RRT-Star algorithms, the proposed method has a higher success rate in dynamic path planning online with less planning time and lower trajectory cost. In addition, a realistic experiment is designed to make UR robotic manipulator avoid human arm random motions dynamically. The experimental results show that the proposed method successfully realizes that robotic manipulator can avoid continuous moving obstacles of human arm online smoothly, comprehensively verifying the effectiveness and superiority.