Trajectory Planning and Active Obstacle Avoidance for Multi-Degree-of-Freedom Manipulator

Abstract

To overcome the challenges of suboptimal path quality, excessive computational overhead, and inadequate obstacle avoidance in multi-degree-of-freedom manipulator trajectory planning, this paper proposes an improved Rapidly-exploring Random Tree (RRT) algorithm based on the Artificial Potential Field (APF) method. By integrating an attractive field from the target point and a repulsive field from obstacles, the enhanced RRT algorithm significantly reduces redundant nodes while improving planning efficiency and safety. Specifically, the attractive field guides random sampling toward the target, thereby shortening the path length, while the repulsive field dynamically adjusts node positions to maintain a safe distance from obstacles. Simulation experiments in both 2D and 3D environments demonstrate the effectiveness of the proposed algorithm, with results showing that the RRT+APF hybrid method outperforms conventional RRT and APF algorithms in terms of path smoothness, computation time, and iteration count. Furthermore, rigorous experimental validation on the JAKA C5 six-degree-of-freedom robotic arm platform confirms the algorithm’s superior performance, producing shorter and smoother trajectories that effectively reduce abrupt changes in joint angles and mechanical shocks.