Obstacle Avoidance Planning and Experimental Study of Reconfigurable Cable-Driven Parallel Robot Based on Deep Reinforcement Learning

Abstract

Cable-driven parallel robot (CDPR) is widely used in the fields of hoisting and cargo handling. However, it is very easy to be interfered with and restricted by obstacles in the space, which affects the working performance of the robot. This paper takes the reconfigurable cable-driven parallel robot (RCDPR) as the research object and adopts deep reinforcement learning (DRL) to solve the obstacle avoidance planning problem. The model of RCDPR is structured, and kinematic analysis is performed to obtain the state transform function. An improved Soft AC algorithm is employed by expected SARSA and adaptive target values, which enhances the utilization of samples. An environment for RCDPR obstacle avoidance tasks is built, and then the improved Soft AC algorithm is used to train the robot to avoid obstacles in the environment. Finally, the simulation and experimental study of the paths and trajectories generated by the policy neural network are performed to verify the feasibility and effectiveness of the proposed algorithm. The results show that RCDPR can realize autonomous planning and intelligent obstacle avoidance by DRL.