基于改进遗传算法的自动导航小车路径规划调度

为解决智能车间物料运输AGV小车调度问题，以AGV补料任务行走总距离最短为目标，结合路径选择及任务排序双重标准，提出双层编码方式；同时为避免染色体上的基因聚集到小的邻域内，提出一种改进的遗传算法，算法增加了多种变异过程，相较于传统遗传算法扩大了解的空间，防止局部最优解的产生。最后通过MATLAB对环境进行建模、仿真，并与基本遗传算法进行对比。实验结果表明：所提出的改进算法能高效且可靠地解决AGV在多任务目标情况下的路径规划问题。

Research on AGV Path Planning Based on Improved Genetic Algorithm

In order to solve the AGV scheduling problem of material transportation in intelligent workshop,taking the shortest total walking distance of AGV replenishment task as the goal,a double-layer coding mode was put forward combining the double standards of path selection and task sequencing.At the same time,in order to avoid the clustering of genes on chromosomes in a small neighborhood,an improved genetic algorithm was proposed,which added a variety of mutation processes.Compared with the traditional genetic algorithm,it enlarged the understanding space and prevented the generation of local optimal solutions.Finally,the environment was modeled and simulated by MATLAB,and compared with the basic genetic algorithm.The experimental results show that the improved algorithm can be used to efficiently and reliably solve the path planning problem of AGV under multi-task target.