面向精度补偿的工业机器人采样点多目标优化

doi:10.13973/j.cnki.robot.2017.0239

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引用本文:
曾远帆, 廖文和, 田威. 面向精度补偿的工业机器人采样点多目标优化[J]. 机器人, 2017, 39(2): 239-248.DOI: 10.13973/j.cnki.robot.2017.0239. ZENG Yuanfan, LIAO Wenhe, TIAN Wei. Multi-objective Optimization of Samples for Industrial Robot Error Compensation. ROBOT, 2017, 39(2): 239-248. DOI: 10.13973/j.cnki.robot.2017.0239.

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．

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	曾远帆
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关键词 ：精度补偿, 最优采样点, 多目标优化, 遗传算法

Abstract：A sample multi-objective optimization method is proposed for the error-similarity-based error compensation method. Firstly, qualitative analysis is performed to show the influence of the samples on error compensation, and the characteristics and mathematical model of the optimal samples are proposed based on the engineering application requirements of error compensation method. Then, a sample multi-objective optimization method based on NSGA-Ⅱ (non-dominated sorting genetic algorithm-Ⅱ) is proposed to solve the optimization problem of sample points. Finally, experiments and contrastive analysis are performed, and it is shown that the maximum positioning error of the robot is reduced from 1.4953 mm to 0.2752mm by using the optimal sample set, which is better than the other 2 random sample sets. The experimental results verify the feasibility and effectiveness of the proposed methods.

Key words： error compensation optimal sample multi-objective optimization genetic algorithm

收稿日期: 2016-12-15 录用日期: 2017-02-15

TP242.2

基金资助:国家自然科学基金（51475225）

通讯作者: 田威,tw_nj@nuaa.edu.cn E-mail: tw_nj@nuaa.edu.cn

作者简介: 曾远帆(1988-),男,博士生.研究领域:机器人精度补偿技术.
廖文和(1965-),男,博士,教授.研究领域:数字化智能制造技术,机器人自动装配技术.
田威(1977-),男,博士,教授.研究领域:智能制造装备,机器人自动装配技术.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2017.0239 或 https://robot.sia.cn/CN/Y2017/V39/I2/239

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