Abstract: In order to get the time optimal trajectory of industrial robot in consideration of the influence of the robot joint constraints, a mixed optimization algorithm is presented for the optimization of multimodal function. Firstly, it locates the vicinage of optimal solution based on chaos search algorithm, then searches the optimal solution within the limits of this vicinage with genetic algorithm. In the MATLAB platform, it programs the mixed algorithm to simulate the trajectory and compares it with the result of traditional genetic algorithm. The execution time of the mixed algorithm is 25.499 s, which is obviously shorter than 39.534 s of the control group. The results prove that the mixed algorithm has better global searching performance.

Key words: industrial robot, trajectory planning, chaotic search algorithm, genetic algorithm, mixed optimization algorithm

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。

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