Manipulator Motion Planning based on Actor-Critic Reinforcement Learning
Qiang Li, Jun Feng Nie, Haixia Wang, Xiao Long Lu, Shibin Song · 2021
The manipulator control model has the characteristics of high-order, nonlinear, multivariable and strong coupling, which makes it difficult for the manipulator to have good adaptability and autonomy. Aiming at the problem of poor reusability and poor autonomy of manipulator applications, a motion planning algorithm based on reinforcement learning is proposed. In this paper, the reinforcement learning continuous control algorithm Actor-Critic is applied to the motion planning of the manipulator to increase the environmental applicability and autonomy of the manipulator, and realize the intelligent control of the manipulator under simple kinematics modeling. At first, the simulation environment of the hand-eye system of the manipulator is constructed, then the reinforcement learning algorithm model is established according to the simulation environment, and finally, the motion planning training of the manipulator is completed in the simulation environment. Experimental results demonstrate that the proposed manipulator motion planning algorithm based on Actor-Critic reinforcement learning has good environmental adaptability and stability.