基于蚁群算法路径规划的物联网仓库管理系统

Fucheng Men, Junmei Guo, Yizhong Luan
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引用次数: 1

摘要

当前涉及存储设备的存储管理过程存在自动引导车辆(AGV)路径规划问题,并且依赖于手动记录货物和信息。因此,存储设施由于记录错误,存储数据保存在纸上,容易丢失,设备运行效率低。因此,本文基于蚁群算法和物联网技术,开发了一种基于蚁群优化(ACO)路径规划的物联网仓库管理系统。该系统通过车载智能终端对叉车的数据进行采集和处理,利用优化的蚁群算法实现叉车的实时定位、超速报警和路径规划。车载终端与仓库管理平台之间通过MQTT协议建立数据传输。最后,通过WEB浏览器对整个系统进行监控和管理,使用MySQL数据库进行持久数据存储。采用开发的非接触式仓库管理方法,将仓库管理过程中分散的数据进行集中和网络化,提高了仓库物流效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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