Deep Reinforcement Learning-based Multi-AMR Path Planning Algorithm

Abstract

Aiming at the scheduling problem of multi-autonomous mobile robots (AMR) in the box storage environment, the traditional dynamic programming (DP) algorithm has the disadvantage of low efficiency in solving the feasible path. To solve this problem, this paper establishes a reinforcement learning (RL) algorithm model with the goal of time optimization, which is used to improve the speed of path planning for multi-AMR simultaneous scheduling. In addition, combined with the advantages of the deep learning (DL) algorithm, the deep reinforcement learning (DRL) algorithm is used to effectively shorten the convergence time of the RL algorithm model training under high-dimensional and complex working conditions. The effectiveness of the DRL method is verified by comparing DP, RL, and DRL algorithm models in the simulation platform.