IoT Warehouse Management System Based on ACO Path Planning

Current storage management processes involving storage equipment suffer from Automated Guided Vehicle (AGV) path planning problems and rely on recording cargo and information manually. Hence, storage facilities present a low equipment operation efficiency due to recording errors and preserving storage data in paper, which is easy to lose. Therefore, this paper develops an Ant Colony Optimization (ACO) path planning-based IoT warehouse management system, relying on an ant colony algorithm and IoT technology. The proposed system collects and processes the forklift’s data through the vehicle’s intelligent terminal and exploits an optimized ant colony algorithm to realize the forklift’s real-time positioning, over-speed alarm, and path planning. Data transmission between the vehicle terminal and the warehouse management platform is established through the MQTT protocol. Finally, the entire system is monitored and managed through a WEB browser, with the MySQL database employed for persistent data storage. Adopting the developed non-contact warehouse management method centralizes and networks the scattered data in the warehouse management process, improving warehouse logistics efficiency.