A Comprehensive Positioning Accuracy Compensation Method Based on BP Neural Network of Industrial Robots

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI:10.1109/ICRAE48301.2019.9043840

Xiangzhen Chen, Q. Zhan, Yifan Wang, Yanbin Yao

{"title":"A Comprehensive Positioning Accuracy Compensation Method Based on BP Neural Network of Industrial Robots","authors":"Xiangzhen Chen, Q. Zhan, Yifan Wang, Yanbin Yao","doi":"10.1109/ICRAE48301.2019.9043840","DOIUrl":null,"url":null,"abstract":"Aiming at the problem that the absolute positioning accuracy of industrial robots cannot meet the requirements of high-precision positioning, a comprehensive positioning accuracy compensation method based on back propagation (BP) neural network was proposed, which considers both the geometric parameters factors and the stiffness performance factors influencing the absolute positioning accuracy of robots. This method uses the actual positioning coordinates and the stiffness performance evaluation index of an industrial robot as the input, and the theoretical positioning coordinates of the robot as output to train a BP neural network. Then the trained BP neural network is used to compensate the absolute positioning accuracy of the robot. This method was tested on a KUKA KR500L340-2 industrial robot, and the experimental results show that the absolute positioning accuracy of the robot is increased from 1.155∽2.892mm before compensation to 0.068∽0.465mm after compensation. The absolute positioning accuracy of the robot has been significantly improved.","PeriodicalId":270665,"journal":{"name":"2019 4th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"91 19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 4th International Conference on Robotics and Automation Engineering (ICRAE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRAE48301.2019.9043840","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

Abstract

Aiming at the problem that the absolute positioning accuracy of industrial robots cannot meet the requirements of high-precision positioning, a comprehensive positioning accuracy compensation method based on back propagation (BP) neural network was proposed, which considers both the geometric parameters factors and the stiffness performance factors influencing the absolute positioning accuracy of robots. This method uses the actual positioning coordinates and the stiffness performance evaluation index of an industrial robot as the input, and the theoretical positioning coordinates of the robot as output to train a BP neural network. Then the trained BP neural network is used to compensate the absolute positioning accuracy of the robot. This method was tested on a KUKA KR500L340-2 industrial robot, and the experimental results show that the absolute positioning accuracy of the robot is increased from 1.155∽2.892mm before compensation to 0.068∽0.465mm after compensation. The absolute positioning accuracy of the robot has been significantly improved.

查看原文本刊更多论文

基于BP神经网络的工业机器人定位精度综合补偿方法

针对工业机器人绝对定位精度不能满足高精度定位要求的问题，提出了一种基于BP神经网络的综合定位精度补偿方法，该方法综合考虑了影响机器人绝对定位精度的几何参数因素和刚度性能因素。该方法以工业机器人的实际定位坐标和刚度性能评价指标为输入，以机器人的理论定位坐标为输出，训练BP神经网络。然后利用训练好的BP神经网络对机器人的绝对定位精度进行补偿。该方法在KUKA KR500L340-2型工业机器人上进行了实验，实验结果表明，该机器人的绝对定位精度由补偿前的1.155∽2.892mm提高到补偿后的0.068∽0.465mm。机器人的绝对定位精度得到了显著提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2019 4th International Conference on Robotics and Automation Engineering (ICRAE)

自引率

0.00%

发文量