Research on Motion Control Method of Manipulator Based on Reinforcement Learning

The traditional motion control algorithm has the problems of poor environmental adaptability and low efficiency. Reinforcement learning can be used to constantly explore trial and error in the environment, and the motion of the manipulator can be controlled by adjusting the neural network parameters through the reward function. However, in reality, it is impossible to provide a trial and error environment for the manipulator. This paper uses the Unity engine platform to build a digital twin simulation environment for the manipulator, set the observation state variables and set the reward function mechanism, and proposes the M-PPO algorithm combining PPO（proximal policy optimization）and multi-agent （agents）in this model environment to speed up the training speed and realize intelligent motion control of the manipulator through reinforcement learning algorithms. This paper completes the effective obstacle avoidance at the end of the manipulator’s execution and reach the target object’s position quickly, and also analyzes the experimental results of the algorithm, M-SAC （multi-agent and soft actor critical）and PPO algorithm. The effectiveness and progressiveness of M-PPO algorithm is verified in the debugging of the manipulator’s motion control decision under different environments. It achieves the purpose of independent planning and decision-making of twins and reverse control of synchronous movement of physical bodies. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd. All rights reserved.