Abstract: To resolve the conflict problem of Automated Guided Vehicles (AGVs) in route scheduling,a guided ant colony particle swarm optimization algorithm was proposed.Based on the calculation of AGVs' travel time,link conflicts and node conflicts were analyzed,and the routing scheduling model for AGVs was built.In the guided ant colony particle swarm optimization algorithm,AGVs were guided to avoid conflicts by adding a guidance factor to the state transition rule.The ant colony optimization was fused with particle swarm optimization to optimize route choice and wait time simultaneously.Simulation experiments with different scale were designed to evaluate the proposed method,and the results indicated that the proposed method could make AGVs avoid link and node conflicts efficiently and improve the security and efficiency of AGVs system.

Key words: automated guided vehicle, path planning, guidance factor, ant colony algorithm, particle swarm algorithm

摘要： 为有效解决多自动导引车路径规划中的冲突问题,提出一种诱导蚁群粒子群算法。在自动导引车行驶时间计算的基础上,分析了路段冲突、节点冲突问题,建立了多自动导引车路径规划模型。在诱导蚁群粒子群算法的状态转移规则中,增加诱导因子来引导自动导引车规避冲突;将蚁群算法与粒子群算法相融合,对路径与等待时间进行同时优化。不同规模算例的仿真结果表明,该算法能有效避免路段冲突与节点冲突,提高多自动导引车系统运行的安全性与效率。

关键词: 自动导引车, 路径规划, 诱导因子, 蚁群算法, 粒子群算法