Intelligent Path Planning for AGV-UAV Transportation in 6G Smart Warehouse

Abstract

Recently, deep reinforcement learning (DRL) has attracted increasing interest in the field of intelligent navigation and path planning in smart warehousing. The latest imitation augmented DRL (IADRL) model has achieved good performance for the cooperative transportation tasks of automatic guided vehicles (AGVs) and unmanned aerial vehicles (UAVs). However, this model cannot always transport target cargoes with the optimized policy due to premature convergence. Therefore, we propose an intelligent path planning model for AGV-UAV transportation in this paper. The proposed model utilizes the proximal policy optimization with covariance matrix adaptation (PPO-CMA) in the imitation learning and DRL networks, which enables the AGV-UAV coalition to plan the optimal transportation route at a lower cost. Experiments conducted in simulation warehousing scenarios demonstrated the proposed model and improved the accumulated training reward by more than 10%, outperforming the existing state-of-the-art models in terms of effectiveness and efficiency.