基于改进遗传算法的移动机械臂拣选路径优化

Optimization of Mobile Manipulator Sorting Path Based on Improved Genetic Algorithm

传统移动机械臂路径规划算法没有根据抓取点分布情况对工位点坐标进行优化,效率低,对此,提出了一种基于改进遗传算法的移动机械臂拣选路径优化方法.通过对拣选物品位置的分析,建立单个工位点上移动机械臂分拣路径模型和多工位点的旅行商(TSP)问题模型,运用改进的遗传算法,在工作空间内对各个工位点的位置坐标寻优,规划出移动机械臂抓取的最短路径和多工位点间移动的最短路径.实验结果表明,与传统遗传算法可能,运用改进的等级进化选择算子和最优近邻交叉算子,遗传算法的收敛速度提高了46.15%,路径缩短了45.99%,系统运行时间减少了25.80%,提高了系统效率.

Path planning is the core technology of mobile manipulators.Aiming at the problem of the traditional fixed-station mobile manipulator planning algorithms,a method for optimizing the picking path of mobile manipulators based on improved genetic algorithm is proposed.By analyzing the position of items to be picked,a sorting path model of mobile manipulators at a single station and a traveling salesman problem(TSP)model for multiple stations are established.An improved genetic algorithm is used to optimize the station coordinates in the workspace,which planned the shortest path grasped by the mobile robot arm and moved between multi-station points.Experiments show that,by using the improved hierarchical evolution selection operator and the optimal nearest neighbor crossover operator,compared with the traditional genetic algorithm,the convergence speed is increased by 46.15%,the path is shortened by45.99%,and the system running time is reduced by 25.80%.That improved system efficiency.