工业机器人定位误差规律分析及基于ELM算法的精度补偿研究

doi:10.13973/j.cnki.robot.170536

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王龙飞, 李旭, 张丽艳, 叶南. 工业机器人定位误差规律分析及基于ELM算法的精度补偿研究[J]. 机器人, 2018, 40(6): 843-851.DOI: 10.13973/j.cnki.robot.170536. WANG Longfei, LI Xu, ZHANG Liyan, YE Nan. Analysis of the Positioning Error of Industrial Robots and Accuracy Compensation Based on ELM Algorithm. ROBOT, 2018, 40(6): 843-851. DOI: 10.13973/j.cnki.robot.170536.

摘要针对工业机器人应用于飞机零部件自动化钻孔时绝对定位精度较差的问题，提出利用极限学习机（ELM）算法建立机器人法兰中心点理论位置与实际位置之间的误差模型，并优化补偿机器人定位精度的方法．首先基于空间网格采样方法，获得了机器人绝对定位误差沿机器人基坐标系不同方向的误差变化规律，分析了建模补偿的可行性；其次建立基于ELM算法的误差补偿模型，并针对误差模型训练中隐含层神经元个数取值问题进行了分析优化．实验结果表明，机器人绝对定位误差值沿其坐标系不同方向存在不同的变化规律，补偿前绝对定位误差分布范围为0.29 mm~0.58 mm，平均误差为0.41 mm；补偿后定位误差分布范围降低到0.04 mm~0.32 mm，平均误差为0.18 mm；采用ELM算法建模的补偿速度快，泛化性能好．

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关键词 ：工业机器人, 绝对定位误差, 误差规律分析, 精度补偿, 空间网格采样, 极限学习机

Abstract：To improve the absolute positioning accuracy of industrial robots in automatic drilling of aircraft parts, an optimized compensation method based on extreme learning machine (ELM) algorithm is proposed to establish the relationship between the nominal position and the actual position of the robot flange center. Firstly, the error variation of the absolute positioning error of the robot in the axial directions of the robot coordinate system is analyzed based on the spatial mesh sampling method, and the feasibility of modeling and compensating the positioning error is verified. Then, the error compensation model based on ELM algorithm is established, and the number of hidden layer neurons is optimized for error model training. Experimental results indicate that the absolute positioning error shows different characteristics in different axial directions. The robot absolute positioning error before compensation is 0.29 mm~0.58 mm, and the average value is 0.41 mm. After compensation, it is reduced to 0.04 mm~0.32 mm, and the average value is 0.18 mm. In addition, the compensation modeling based on ELM algorithm is of high speed and good generalization performance.

Key words： industrial robot absolute positioning error error analysis accuracy compensation spatial mesh sampling extreme learning machine

收稿日期: 2017-09-13 录用日期: 2018-03-07

TP242.2

基金资助:国家自然科学基金（51575276）

通讯作者: 张丽艳,zhangly@nuaa.edu.cn E-mail: zhangly@nuaa.edu.cn

作者简介: 王龙飞(1993-),男,硕士生.研究领域:机器人误差补偿
李旭(1993-),男,硕士生.研究领域:机器人误差补偿
张丽艳(1967-),女,博士,教授,博士生导师.研究领域:数字化制造,机器视觉,CAD/CAM等.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.170536 或 https://robot.sia.cn/CN/Y2018/V40/I6/843

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