Deep Reinforcement Learning-based Trajectory Planning for Manipulator Obstacle Avoidance
Cao Yi
Guo Yinhui
Li Lei
Zhu Baiyu
Zhao Zhihua
Abstract：A deep reinforcement learning(DRL)-based motion planning method is proposed to improve long planning elapse and lengthy path of the traditional planning algorithms for robotic manipulator movement in obstacle avoidance.Firstly,based on the mathematical model of the manipulator and the motion environment,the DOBOT robot and the operating environment are built in PyBullet,and the parameters such as the reward function,the action and the state variables required for DRL are set.Secondly,the deep deterministic policy gra-dient(DDPG)algorithm is applied for the characteristics of static obstacle avoidance,and motion simulation ex-periments are conducted.The simulation results show that the proposed DDPG algorithm has a certain degree of improvements in planning elapse and path length compared with the rapid-exploring random tree(RRT)algo-rithm and the improved RRT algorithm.Finally,the effectiveness of the DDPG algorithm in obstacle avoidance operations is tested using the DOBOT robot in a laboratory environment with multiple obstacles.
Keywords：ManipulatorDeep reinforcement learningObstacle avoidance path planningDeep de-terministic policy gradient algorithm
Publication Date：2023-12-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 40-46,96 )