基于膨胀栅格的多目标站点AGV路径规划方法

Research on the Method of AGV Path Planning With Multiple Target Station Based on Expansion Grid

针对离散制造、环境多变的脉动装配线特点,以及常规栅格建模环境下自动导引车(Automated Guided Vehicle,ACV)单一目标运动的局限性,提出一种膨胀栅格建模的改进方法,并结合蚁群算法进行优化与应用。首先在常规栅格基础上增设膨胀栅格进行建模,以此适应环境多变的脉动生产线特点;然后,在传统路径规划的蚁群算法基础上,引入基于精英策略的信息素更新方法与最大最小蚂蚁系统,提出改进的多任务轨迹规划算法。经仿真实验验证,优化蚁群算法在整体路径长度与收敛性上优于传统算法和其他路径规划算法。仿真实验还分别模拟了分段路径与不同占比障碍栅格单元的优化修正效果:当分成若干段路径时,中转站越靠近障碍栅格,优化算法影响越大;当障碍栅格占比不同时,在路径可控范围内的障碍栅格占比越高,优化算法的效果越理想。

Aiming at the characteristics of discrete manufacturing,variable environment of pulsatile assembly lines,and the limitations of single target motion of automated guided vehicles(AGVs)in the conventional grid modeling environment,an improved method of expansion grid modeling is proposed,which is optimized and applied combined with ant colony algorithm.Firstly,an expansion grid is added to the conventional grid for modeling,so as to adapt to the characteristics of the fluctuating environment of the production line.Then,based on the traditional ant colony algorithm for path planning,an improved multi-task trajectory planning algorithm is proposed by introducing the pheromone update method based on elite strategy and the max-min ant system.The simulation results show that the optimized ant colony algorithm is superior to the traditional algorithm and other path planning algorithms in the overall path length and convergence.The simulation experiment also simulates the optimization correction performance of the segmented path and the obstacle grid cells with different proportions.When the path is divided into several segments,the closer the transfer station is to the obstacle grid,the greater the influence of the optimization algorithm.When the proportion of the obstacle grids is different,the higher the proportion of the obstacle grids in the controllable range of the path is,the better the performance of the optimization algorithm is.