A Calibration and Compensation Method Based on ELM for Aviation Drilling Robot

XUE Lei; LI Rupeng; ZHOU Qinghui; TANG Haiyang; FANG Wei

doi:10.16080/j.issn1671-833x.2017.22.091

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A Calibration and Compensation Method Based on ELM for Aviation Drilling Robot

更新时间：2025-10-30

- A Calibration and Compensation Method Based on ELM for Aviation Drilling Robot
- Aeronautical Manufacturing Technology Vol. 60, Issue 22, Pages: 91-95(2017)
- 作者机构：
  
  1. 上海飞机制造有限公司,上海,200436
  2. 北京航空航天大学机械工程及自动化学院,北京,100191
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671-833x.2017.22.091
  CLC：
- Published：2017
- 稿件说明：
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薛雷1，李汝鹏1，周庆慧1，汤海洋1，方伟1，陈冬冬2，袁培江2. 基于ELM 的航空制孔机器人定位精度补偿方法[J]. 航空制造技术, 2017, 60(22): 91-95.

XUE Lei1, LI Rupeng1, ZHOU Qinghui1, TANG Haiyang1, FANG Wei1, CHEN Dongdong2, YUAN Peijiang2. A Calibration and Compensation Method Based on ELM for Aviation Drilling Robot. Aeronautical Manufacturing Technology, 2017, 60(22): 91-95.
薛雷1，李汝鹏1，周庆慧1，汤海洋1，方伟1，陈冬冬2，袁培江2. 基于ELM 的航空制孔机器人定位精度补偿方法[J]. 航空制造技术, 2017, 60(22): 91-95. DOI： 10.16080/j.issn1671-833x.2017.22.091.

XUE Lei1, LI Rupeng1, ZHOU Qinghui1, TANG Haiyang1, FANG Wei1, CHEN Dongdong2, YUAN Peijiang2. A Calibration and Compensation Method Based on ELM for Aviation Drilling Robot. Aeronautical Manufacturing Technology, 2017, 60(22): 91-95. DOI： 10.16080/j.issn1671-833x.2017.22.091.

摘要

针对航空制孔机器人绝对定位精度补偿中存在的建模复杂及运算量大的问题，提出了一种基于极限学习机的绝对定位精度补偿方法。该方法通过将机器人视为一个黑箱系统，忽略机器人的几何因素和非几何因素的影响，通过高精度的激光跟踪仪测量获得机器人的末端运动误差，采用极限学习机建立机器人误差预测模型。由机器人误差预测模型获得机器人在期望位置的位置偏差，通过修正机器人位置坐标来实现机器人的绝对定位精度补偿。最后该方法在航空制孔机器人上进行了试验，试验结果显示机器人的绝对位置误差的平均值和最大值分别降低了75.69% 和78.16%。

Abstract

In order to solve the problems that modeling was complex and a large computation was costed for calibration and compensation of aviation drilling robot

a calibration and compensation method based on extreme learning machine was proposed. The aviation drilling robot was regarded as a black-box system in this method which ignored the influence of geometric factors and non-geometric factors of robot. Then

according to robot positional errors measured by a high-accuracy laser tracker

a robot error prediction model based on extreme learning machine was trained and established. Next

the positional error in desired position could be predicted by robot error prediction model and the robot position was compensated to achieve the robot calibration. Final

experimental studies were carried on an aviation drilling robot. The experimental results showed that the mean and maximum positional error of robot was reduced by 75.69% and 78.16%

respectively.

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