面向精度补偿的工业机器人采样点多目标优化

发布时间：2018-03-10 11:15

  本文选题：精度补偿　切入点：最优采样点　出处：《机器人》2017年02期 　论文类型：期刊论文

【摘要】：针对基于误差相似性的机器人精度补偿方法,提出一种机器人采样点的多目标优化方法.首先,定性分析了采样点对于精度补偿效果的影响,并根据精度补偿的工程应用需求,提出了最优采样点的特征和数学模型.其次,为解决最优采样点的优化问题,提出了基于NSGA-Ⅱ(快速非支配排序遗传算法)的采样点多目标优化方法.最后,试验验证和比较分析表明,最优采样点能够将机器人的最大定位误差由1.4953 mm降低至0.2752 mm,补偿效果优于另外2组随机采样点,验证了本文方法的可行性和有效性.
[Abstract]:Aiming at the accuracy compensation method of robot based on error similarity, a multi-objective optimization method for robot sampling points is proposed. Firstly, the effect of sampling points on precision compensation effect is analyzed qualitatively, and according to the engineering application requirements of precision compensation. The characteristics and mathematical model of optimal sampling points are proposed. Secondly, in order to solve the optimization problem of optimal sampling points, a multi-objective optimization method of sampling points based on NSGA- 鈪，

本文编号：1593097