Task Scheduling and Path Planning of Multiple AGVs via Cloud and Edge Computing

Abstract

Automated Guided Vehicles (AGVs) are playing a crucially important role in automated warehousing and logistics. To achieve high efficiency of management and operation of large-scale AGV systems, we design a steady hierarchical cloud–edge control platform in this paper. The platform has been characterized by load balancing, service reconfiguration, and stability, and low latency of communication between the cloud and AGVs. Also, we adapt a parallelized scheduling algorithm as a cloud service for a large-scale AGV system to attain higher algorithm performance. Next, a parallel path planning algorithm is designed at the edge to plan conflict-free paths for single AGVs. Finally, many experiments are conducted and the results show the effectiveness and superiority of the presented methods.