A Two-Stage Parameter Identification Method and Compensation Verification for Heavy Load Robot

Zhirong Wang, Chentao Mao, Zhang-wei Chen, Yuxiang Wang, Jun Zhou, Zhen Lu
{"title":"A Two-Stage Parameter Identification Method and Compensation Verification for Heavy Load Robot","authors":"Zhirong Wang, Chentao Mao, Zhang-wei Chen, Yuxiang Wang, Jun Zhou, Zhen Lu","doi":"10.1109/CRC.2019.00017","DOIUrl":null,"url":null,"abstract":"Kinematic calibration plays a significant role in improving the robot positioning accuracy. Due to the mechanical factors such as joint deformation and heavy load, some errors are non-geometrical and nonlinear. In this case, a single kinematic calibration method is difficult to achieve good results especially for robots with heavy load. In this paper, a two-stage parameter identification method is proposed, which also deals with joint deformation and heavy load dependent errors to achieve a higher positioning accuracy. In the first stage, the method builds the kinematic model and identifies the geometric errors using the DH model and the distance model. In the second stage, the stiffness of the joint is analyzed and the positioning accuracy is improved on the robot dynamics. Finally, the experiments are carried out on the 6R robot with heavy load. The experimental results demonstrate the effectiveness and correctness of the method.","PeriodicalId":414946,"journal":{"name":"2019 4th International Conference on Control, Robotics and Cybernetics (CRC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 4th International Conference on Control, Robotics and Cybernetics (CRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRC.2019.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Kinematic calibration plays a significant role in improving the robot positioning accuracy. Due to the mechanical factors such as joint deformation and heavy load, some errors are non-geometrical and nonlinear. In this case, a single kinematic calibration method is difficult to achieve good results especially for robots with heavy load. In this paper, a two-stage parameter identification method is proposed, which also deals with joint deformation and heavy load dependent errors to achieve a higher positioning accuracy. In the first stage, the method builds the kinematic model and identifies the geometric errors using the DH model and the distance model. In the second stage, the stiffness of the joint is analyzed and the positioning accuracy is improved on the robot dynamics. Finally, the experiments are carried out on the 6R robot with heavy load. The experimental results demonstrate the effectiveness and correctness of the method.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
重载机器人两阶段参数辨识方法及补偿验证
运动学标定对提高机器人定位精度具有重要意义。由于关节变形和重载等力学因素的影响,有些误差是非几何的和非线性的。在这种情况下,单一的运动学标定方法很难获得良好的结果,特别是对于负载较大的机器人。为了实现更高的定位精度,本文提出了一种两阶段参数辨识方法,同时考虑了关节变形和重载相关误差。在第一阶段,该方法建立运动学模型,并使用DH模型和距离模型识别几何误差。第二阶段对关节刚度进行分析,从机器人动力学角度提高定位精度。最后,在6R重型机器人上进行了实验。实验结果证明了该方法的有效性和正确性。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The Literature Review on Control Methods of SOH and SOC for Supercapacitors Trajectory-Based Air-Writing Character Recognition Using Convolutional Neural Network Triboelectric Nanogenerator and Integration with Electrochemical Microsupercapacitor An Overview of Extreme Learning Machine Methods for Switching Multiple Speeds on a Single Link
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1