A Trajectory Planning Method for Capture Operation of Space Robotic Arm Based on Deep Reinforcement Learning

Abstract

Abstract In order to deal with the complex dynamics and control problems involved in space debris removal, a trajectory planning technique for a spatial robotic arm based on twin delayed DDPG (TD3) in deep reinforcement learning is proposed, and it can accomplish an end-to-end control effect comparable to that of human hand gripping objects. The trajectory planning method for capturing space debris by a floating-base space robotic arm is realized using a space robotic arm task simulation platform built on MuJoCo and using trajectory planners, trajectory trackers, and joint and end-effector control strategies formulated with seven different weighted reward functions. This makes it easier to complete spacecraft in-orbit servicing and maintenance missions. The experiment results demonstrate that the capture strategy can maintain a capture success rate of more than 99%, and debris capture can be mostly finished in three stages when taking the stability of the floating base into consideration by continuously modifying the trajectory.