作者:秦昌礼，张华强，刘林，陈雨，苏庆华

Authors:QIN Chang-li,ZHANG Hua-qiang,LIU Lin,CHEN Yu,SU Qing-hua
摘要:摘要:针对粒子群优化算法在全局路径规划时存在容易陷入局部最优的问题，依据粒子群算法和鸽群算法的相关理论，分析了导致粒子群算法陷入局部最优的影响因素，根据AGV小车的最小转向半径确定二维栅格地图中单元格的大小，改进粒子群算法并结合鸽群算法的快速收敛能力进行全局路径规划，给出了一种融合改进粒子群与鸽群算法的二阶段混合优化算法。对比传统的粒子群算法，新方法能有效防止算法陷入局部最优，规划的全局最优路径长度比传统的粒子群算法缩短了约3.8%并减少了路径规划时长。

Abstract:Abstract:In order to solve the problem of particle swarm optimization algorithm easily falling into local optimum in global path planning, according to the related theories of particle swarm optimization algorithm and pigeon-inspired optimization algorithm, the influencing factors that lead to particle swarm optimization algorithm falling into local optimum are analyzed, and the size of raster in two-dimensional raster map is determined according to the minimum turning radius of automatic guided vehicle.The particle swarm optimization is improved and combined with the fast convergence ability of pigeon-inspired optimization algorithm, and a two-stage hybrid optimization algorithm based on improved particle swarm optimization and pigeon-inspired optimization algorithm is proposed for global path planning. Compared with the traditional particle swarm optimization algorithm, the new method can effectively avoid falling into local optimum. The global optimal path length is shortened by about 3.8% and the path planning time is reduced.

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