A Distributed Reward Algorithm for Inverse Kinematics of Arm Robot

Abstract

Traditional methods of inverse kinematics of robots always adopt analytical approach and numerical approach to solve the continuous state and action problems with experience and experiment mostly, which require much time and work in reality work scene, especially for robots with complex structure. This paper proposes a method based on reinforcement learning TD3 network, which is constructed by PyTorch to find the inverse solution from another point of view. A set of improved distributed multiple rewards which choose the position difference between adjacent joints as the reward standard are designed to optimize the solution, avoid solving unreachable points and prevent the mechanical structure from being damaged also in the environment of five-degree-of-freedom arm robot. The validity of above method is verified by simulation experiment results.