A Motion Planning Algorithm for Live Working Manipulator Integrating PSO and Reinforcement Learning Driven by Model and Data

Abstract

To solve the motion planning of the live working manipulator, this research proposes a hybrid data-model–driven algorithm called the P-SAC algorithm. In the model-driven part, to avoid obstacles and make the trajectory as smooth as possible, we designed the trajectory model of the sextic polynomial and used the PSO algorithm to optimize the parameters of the trajectory model. The data generated by the model-driven part are then passed into the replay buffer to pre-train the agent. Meanwhile, to guide the manipulator in reaching the target point, we propose a reward function design based on region guidance. The experimental results show that the P-SAC algorithm can reduce unnecessary exploration of reinforcement learning and can improve the learning ability of the model-driven algorithm for the environment.