基于改进混沌搜索算法的机器人轨迹规划  

Planning and simulation of robot optimal trajectory based on improved chaotic search algorithm

作　　者：康代轲 陈明[2] KANG Daike;CHEN Ming(School of Mechanical Engineering, Tongji University, Shanghai 201804, China;Sino-German College Applied Sciences, Tongji University, Shanghai 201804, China)

机构地区：[1]同济大学机械与能源工程学院,上海201804 [2]同济大学中德工程学院,上海201804

出　　处：《计算机工程与应用》2017年第14期143-147,共5页Computer Engineering and Applications

摘　　要：在考虑机器人关节约束的影响下,为得到工业机器人的时间最优轨迹,提出了一种适用于多极值函数优化问题的混合算法。首先基于混沌搜索算法定位最优解的邻域,继而使用遗传算法在此邻域内寻找最优解。在MATLAB平台上,对该混合算法进行编程并仿真轨迹,并与传统遗传算法的结果进行比较,结果表明使用混合算法得到的总时间为25.449 s,明显少于对照组的39.534 s,证实了该混合算法具有较好的全局搜索性能。In order to get the time optimal trajectory of industrial robot in consideration of the influence of the robot jointconstraints,a mixed optimization algorithm is presented for the optimization of multimodal function.Firstly,it locates thevicinage of optimal solution based on chaos search algorithm,then searches the optimal solution within the limits of thisvicinage with genetic algorithm.In the MATLAB platform,it programs the mixed algorithm to simulate the trajectory andcompares it with the result of traditional genetic algorithm.The execution time of the mixed algorithm is25.499s,whichis obviously shorter than39.534s of the control group.The results prove that the mixed algorithm has better global searching performance.

关 键 词：工业机器人 轨迹规划 混沌搜索 遗传算法 混合优化算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]