Cai Zhengying, Du Jingshu, Huang Tianhao, Lu Zhuimeng, Liu Zeya, Gong Guoqiang
Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.
Sensors (Basel). 2024 Dec 17;24(24):8044. doi: 10.3390/s24248044.
With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing method based on vehicle edge intelligence to solve the energy-efficient collision-free machine/AGV scheduling problem. First, a vehicle edge intelligence architecture was built, and the corresponding state transition diagrams for collision-free scheduling were developed. Second, the energy-efficient collision-free machine/AGV scheduling problem was modeled as a multi-objective function with electric capacity constraints, where production efficiency, collision prevention, and energy conservation were comprehensively considered. Third, an artificial plant community algorithm was explored based on the edge intelligence of AGVs. The proposed method utilizes a heuristic search and the swarm intelligence of multiple AGVs to realize energy-efficient collision-free scheduling and is suitable for deploying on embedded platforms for edge computing. Finally, a benchmark dataset was developed, and some benchmark experiments were conducted, where the results revealed that the proposed heuristic method could effectively instruct multiple automatic guided vehicles to avoid collisions with high energy efficiency.
随着自动导引车（AGV）的广泛应用，避免碰撞已成为一个具有挑战性的问题。解决这个问题并非易事，因为生产效率、防撞和能耗是相互冲突的因素。本文提出了一种基于车辆边缘智能的新型边缘计算方法，以解决节能无碰撞机器/AGV调度问题。首先，构建了车辆边缘智能架构，并开发了相应的无碰撞调度状态转移图。其次，将节能无碰撞机器/AGV调度问题建模为具有电容约束的多目标函数，综合考虑了生产效率、防撞和节能。第三，基于AGV的边缘智能探索了一种人工植物群落算法。所提方法利用启发式搜索和多个AGV的群体智能来实现节能无碰撞调度，适用于在边缘计算的嵌入式平台上部署。最后，开发了一个基准数据集，并进行了一些基准实验，结果表明所提启发式方法能够有效地指导多个自动导引车以高能源效率避免碰撞。
