本文選題：精度補償　切入點：最優(yōu)采樣點　出處：《機器人》2017年02期 　論文類型：期刊論文

【摘要】：針對基于誤差相似性的機器人精度補償方法,提出一種機器人采樣點的多目標優(yōu)化方法.首先,定性分析了采樣點對于精度補償效果的影響,并根據(jù)精度補償?shù)墓こ虘?yīng)用需求,提出了最優(yōu)采樣點的特征和數(shù)學(xué)模型.其次,為解決最優(yōu)采樣點的優(yōu)化問題,提出了基于NSGA-Ⅱ(快速非支配排序遺傳算法)的采樣點多目標優(yōu)化方法.最后,試驗驗證和比較分析表明,最優(yōu)采樣點能夠?qū)C器人的最大定位誤差由1.4953 mm降低至0.2752 mm,補償效果優(yōu)于另外2組隨機采樣點,驗證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，

本文編號：1593097