Target Area Guided Manipulator Path Planning of RRT*

Abstract

A target area guided RRT* robotic arm path planning algorithm (TA-RRT*) is proposed to address the issues of low planning efficiency, poor path quality, and improper robotic arm pose in the traditional RRT* algorithm for robotic arm path planning. Firstly, with the traditional RRT* algorithm as the foundation, a target bias strategy is introduced and a spherical subset constraint sampling is utilized to narrow the sampling range and guide the expansion of the new node towards the target point, enhancing target orientation. By employing a direct connection strategy for new nodes, the algorithm is enabled to converge faster and the speed of path generation is improved. Secondly, by removing redundant points from the initial planning path and transforming it into a smooth path using a cubic B-spline curve, the quality of the path is improved. Finally, the position of the robotic arm is constrained. The reachability of the robotic arm linkage pose is ultimately determined through the inverse kinematics of the robotic arm, and the envelope box model is used to determine whether the robotic arm is collided with obstacles. Experimental results show that the TA-RRT* algorithm outperforms the RRT* algorithm in terms of sampling frequency, planning time, path length, and smoothness in 2D and 3D scenes, verifying the correctness and feasibility of this method. Both the robotic arm simulation experiments and the test results in real environment demonstrate that when adding pose constraints to the planned trajectory of the robotic arm during operation, the joints of the robotic arm do not collide with obstacles during the execution of the planned paths and exhibit good stability.

Keywords

• rrt* algorithm; manipulator path planning; target area guide; cubic b-spline curve