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计量学报  2021, Vol. 42 Issue (9): 1128-1135    DOI: 10.3969/j.issn.1000-1158.2021.09.02
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融合加权SVD改进算法的工业机器人运动学标定
班朝1,2,任国营2,3,王斌锐1,陈相君3,薛梓2,王凌1
1.中国计量大学 机电工程学院,浙江 杭州 310018
2.中国计量科学研究院,北京 100029
3.天津大学 精密测试技术及仪器国家重点实验室,天津 300072
Kinematics Calibration of Industrial Robot Fusing Weighted SVD Algorithm
BAN Zhao1,2,REN Guo-ying2,3,WANG Bin-rui1,CHEN Xiang-jun3,XUE Zi2,WANG Ling1
1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. National Institute of Metrology, Beijing 100029, China
3. State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China
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摘要 针对环境或人为因素引入的测量粗差对测量坐标系和机器人基坐标系的转换存在较大影响的问题,对奇异值分解(SVD)算法进行了改进,并将其应用于机器人运动学标定中。以ABB-IRB2600型机器人为研究对象,建立修正型D-H(MD-H)运动学模型和误差模型;通过激光跟踪仪测量得到机器人末端靶球位置坐标,在SVD算法中,根据补偿前位置误差大小对测量数据重新分配权重,转换测量坐标系和机器人基坐标系;使用Levenberg-Marquart(L-M)算法进行了误差参数辨识,并在Matlab中对机器人25个运动学参数进行了仿真补偿。仿真和实验结果表明,加权SVD算法稳定性更优,能够减小测量粗差影响,经标定后机器人的平均绝对误差降低了65.10%,均方根误差降低了65.85%,其绝对定位精度得到了明显提高。
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班朝
任国营
王斌锐
陈相君
薛梓
王凌
关键词 计量学工业机器人运动学标定加权SVD算法L-M算法    
Abstract:Aiming at the problem that the gross error of measurement introduced by environmental or human factors has a great influence on the conversion of measurement coordinate system and base coordinate system of robot, a method is proposed that the singular value decomposition (SVD) algorithm is improved and applied to the robot kinematics calibration. Taking ABB-IRB2600 robot as the research object, modified D-H (MD-H) kinematics model and error model were established. The position coordinates of the target sphere at the end of robot were measured by the laser tracker. In the SVD algorithm, the weight of the measured data was redistributed according to the position error before compensation, and the measurement coordinate system and the robot base coordinate system were converted. Levenberg-Marquardt (L-M) algorithm was used to identify the error parameters, and 25 kinematic parameters of the robot were simulated and compensated in Matlab. Simulation and experimental results show that the weighted SVD algorithm has better stability and can reduce the impact of gross errors. After calibration, for the average absolute error of the robot is reduced by 65.10% and the root mean square error by 65.85%, and its absolute positioning accuracy is obviously improved after calibration.
Key wordsmetrology    industrial robot    kinematics calibration    weighted    SVD algorithm    L-M algorithm
收稿日期: 2020-07-28      发布日期: 2021-09-24
PACS:  TB92  
  TB973  
基金资助:国家重点研发计划(2018YFF0212701,2018YFF0212702,2018YFB2101004)
通讯作者: 任国营(1979-),河南开封人,中国计量科学研究院副研究员,主要从事长度计量、机器人测试方面研究。Email:rengy@nim.ac.cn     E-mail: rengy@nim.ac.cn
作者简介: 班朝(1996-),安徽无为人,中国计量大学硕士研究生,主要研究方向为惯性导航、机器人标定技术。Email: joahban@163.com
引用本文:   
班朝,任国营,王斌锐,陈相君,薛梓,王凌. 融合加权SVD改进算法的工业机器人运动学标定[J]. 计量学报, 2021, 42(9): 1128-1135.
BAN Zhao,REN Guo-ying,WANG Bin-rui,CHEN Xiang-jun,XUE Zi,WANG Ling. Kinematics Calibration of Industrial Robot Fusing Weighted SVD Algorithm. Acta Metrologica Sinica, 2021, 42(9): 1128-1135.
链接本文:  
http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2021.09.02     或     http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2021/V42/I9/1128
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