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.

中文翻译：

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6G智能仓库AGV-UAV运输智能路径规划

最近，深度强化学习（DRL）在智能仓储的智能导航和路径规划领域引起了越来越多的关注。最新的仿增强DRL（IADRL）模型在自动导引车（AGV）和无人机（UAV）的协同运输任务中取得了良好的性能。然而，由于过早收敛，该模型不能总是使用优化策略运输目标货物。因此，我们在本文中提出了一种用于 AGV-UAV 运输的智能路径规划模型。所提出的模型在模仿学习和 DRL 网络中利用具有协方差矩阵自适应 (PPO-CMA) 的近端策略优化，使 AGV-UAV 联盟能够以较低的成本规划最佳运输路线。