Integrated reinforcement learning of automated guided vehicles dynamic path planning for smart logistics and operations

Automated guided vehicles (AGV) play a critical role in fostering a smarter logistics and operations environment. Conventional path planning for AGVs enables the load-in-load-out of the items, but existing approaches rarely consider dynamic integrations with smart warehouses and factory systems. Therefore, this study presents a reinforcement learning (RL) approach for real-time path planning in automated guided vehicles within smart warehouses or smart factories. Unlike conventional path planning methods, which struggle to adapt to dynamic operational changes, the proposed algorithm integrates real-time information to enable responsive and flexible routing decisions. The novelty of this study lies in integrating AGV path planning and RL within a dynamic environment, such as a smart warehouse containing various workstations, charging stations, and storage locations. Through various scenarios in smart factory settings, this research demonstrates the algorithm's effectiveness in handling complex logistics and operations environments. This research advances AGV technology by providing a scalable solution for dynamic path planning, enhancing efficiency in modern industrial systems.