Genetic Scheduling and Reinforcement Learning in Multirobot Systems for Intelligent Warehouses

Abstract

A new hybrid solution is presented to improve the efficiency of intelligent warehouses with multirobot systems, where the genetic algorithm (GA) based task scheduling is combined with reinforcement learning (RL) based path planning for mobile robots. Reinforcement learning is an effective approach to search for a collision-free path in unknown dynamic environments. Genetic algorithm is a simple but splendid evolutionary search method that provides very good solutions for task allocation. In order to achieve higher efficiency of the intelligent warehouse system, we design a new solution by combining these two techniques and provide an effective and alternative way compared with other state-of-the-art methods. Simulation results demonstrate the effectiveness of the proposed approach regarding the optimization of travel time and overall efficiency of the intelligent warehouse system.