控制工程 ›› 2019, Vol. 26 ›› Issue (12): 2315-2400.

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基于MOWCA的多模式自动导引车路径优化算法

  

  • 出版日期:2019-12-20 发布日期:2023-11-29

Path optimization algorithm of Multi-mode Automatic Guided Vehicle Based on MOWCA

  • Online:2019-12-20 Published:2023-11-29

摘要: 为提高自动导引车(Automated Guided Vehicle, AGV)系统路径优化效果,提出一种基于多目标狼群算法(Multi-Objective Wolf Colony Algorithm, MOWCA)的多模式自动导引车路径优化方法。首先对AGV路径规划问题进行研究,给出其多目标优化函数,并设计了两阶段的AGV路径优化系统;其次引入狼群算法(Wolf Colony Algorithm, WCA),利用非支配状态的狼群个体进行多目标优化算法设计,并利用种群个体密度对种群多样性进行保持,实现了多目标算法性能的提升;最后通过在长方形区域障碍物上的仿真实验,对算法的AGV规划设计性能进行了验证,实现了AGV运行路线的有效规划。

关键词:

自动导引车, 多目标优化, 狼群算法, 路径优化

Abstract: In order to improve the path optimization effect of AGV, a multi model AGV route optimization method based on multi-objective wolf pack algorithm is proposed. Firstly, the AGV path planning problem is studied, and the multi-objective optimization function is given, and the two stage AGV path optimization system is designed; Secondly, the introduction of the wolves algorithm, using non-dominant wolves individuals for multi-objective optimization algorithm design, and using population individual density to maintain population diversity,, achieved the performance improvement of the multi-objective algorithm; Finally, the AGV programming performance of the algorithm is validated by the simulation experiments in the rectangular area obstacle, and the effective planning of the AGV running route is realized.

Key words:

 AGV; multi-objective optimization, wolf pack algorithm, path optimization system