基于改进蚁群算法的机器人末端路径排序优化

Path Sorting Optimization of Robotic End-effector by Improved ACA

建立了针对机器人加工时的末端运动路径排序优化问题的数学模型,将该模型转化为广义旅行商问题并用蚁群算法求解。同时对经典的蚁群算法进行了改进,即采用多阶段搜索策略、邻域搜索策略及多蚁种搜索策略,使改进后的蚁群算法能为机器人求取一条更优的末端运动路径。计算机仿真与机器人加工实验结果表明,改进蚁群算法所得的末端运动路径比基本蚁群算法所得结果缩短了3%以上。

For the path sorting optimization of robotic end-effector in robotic machining,a solution was presented,that established mathematical model for this problem and converted it to generalized traveling salesman problem（GTSP）and solved this problem by ACA.Meanwhile,the classical ACA was improved with multi stage search strategy,neighborhood search strategy and multi ant type strategy,so that the improved ACA was able to calculate a more optimized end-effector path for robotic machining.The results of simulation and robotic machining prove that the end-effector path obtained by improved ACA is shorter than 3% above the basic ACA＇s.