[1]王柴志,尹方辰,黄辉,等.串联机器人加工系统关节刚度的高精度辨识方法[J].华侨大学学报(自然科学版),2019,40(6):707-715.[doi:10.11830/ISSN.1000-5013.201905031]
　WANG Chaizhi,YIN Fangchen,HUANG Hui,et al.High Accuracy Identification Method of Joint Stiffness in Serial Robot Machining System[J].Journal of Huaqiao University(Natural Science),2019,40(6):707-715.[doi:10.11830/ISSN.1000-5013.201905031]
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串联机器人加工系统关节刚度的高精度辨识方法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第40卷
期数:
2019年第6期
页码:
707-715
栏目:
出版日期:
2019-11-20

文章信息/Info

Title:
High Accuracy Identification Method of Joint Stiffness in Serial Robot Machining System
文章编号:
1000-5013(2019)06-0707-09
作者:
王柴志;  尹方辰;  黄辉;  黄身桂
华侨大学 制造工程研究院, 福建 厦门 361021
Author(s):
WANG Chaizhi;  YIN Fangchen; HUANG Hui;  HUANG Shengui
Institution of Manufacture Engineering, Huaqiao University, Xiamen 361021, China
关键词:
串联机器人;  加工系统;  灵巧性;  关节刚度;  刚度识别;  端部变形预测
Keywords:
serial robot;  machining system;  dexterity;  joint stiffness;  stiffness identification;  end deformation prediction
分类号:
TP242
DOI:
10.11830/ISSN.1000-5013.201905031
文献标志码:
A
摘要:
提出一种串联机器人加工系统关节刚度的高精度辨识方法.首先,基于雅克比矩阵的Frobenius范数,得到串联机器人加工系统的灵巧性,并确定工作空间内可达姿态的灵巧性数值分布情况.然后,分别选取4组不同等级灵巧性的姿态进行关节刚度辨识实验,并计算出相应姿态下的关节刚度.最后,对辨识出的4组关节刚度的末端变形计算值与测量值进行相对误差分析.结果表明:随着灵巧性的增大,机器人关节刚度辨识的准确性越高;相较于灵巧性较小的姿态,灵巧性较大的姿态的末端变形计算值与测量值的相对误差可降低20%~50%.
Abstract:
A high-precision joint stiffness identification method for a serial robot machining system was proposed. First, based on the Frobenius model number of the Jakobi matrix, the kinematic conditioning index of system was obtained and the numerical distribution of kinematic conditioning index at the robot accessible postures in the working space was determined. Then, four groups of postures with different levels of kinematic conditioning index were selected to carry out the joint stiffness identification experiment, and the joint stiffness was calculated under the corresponding posture. Finally, the relative error between the end deformation calculation value and measurement value of the identified four groups of joint stiffness was analyzed. The results show that the accuracy of robot joint stiffness recognition increases with the increase of kinematic conditioning index, and the relative error is reduced by 20%-50% compared to that at the posture with less kinematic conditioning index.

参考文献/References:

[1] CHEN S F,KAO I.Geometrical approach to the conservative congruence transformation(CCT)for robotic stiffness control[C]//IEEE International Conference on Robotics and Automation.Washington D C:IEEE Press,2002:544-549.DOI:10.1109/ROBOT.2002.1013415.
[2] ALICI G,SHIRINZADEH B.Enhanced stiffness modeling, identification and characterization for robot manipulators[J].IEEE Transactions on Robotics,2005,21(4):554-564.DOI:10.1109/TRO.2004.842347.
[3] DUMAS C,CARO S,GARNIER S,et al.Joint stiffness identification of six-revolute industrial serial robots[J].Robotics and Computer-Integrated Manufacturing,2011,27(4):881-888.DOI:10.1016/j.rcim.2011.02.003.
[4] 陈玉山.6R型工业机器人关节刚度辨识与实验研究[D].武汉:华中科技大学,2011.DOI:10.7666/d.d190692.
[5] 王旭,李东升,王明明.工业机器人大负载刚度辨识及误差补偿研究[J].机械传动,2017,41(5):39-42.DOI:10.16578/j.issn.1004.2539.2017.05.007.
[6] 刘本德.基于D-H参数精确标定的工业机器人关节刚度辨识[D].天津:天津大学,2014.DOI:10.7666/d.D6548 57.
[7] ZARGARBASHI S H H,KHAN W,ANGELES J.The Jacobian condition number as a dexterity index in 6R machining robots[J].Robotics and Computer-Integrated Manufacturing,2012,28(6):694-699.DOI:10.1016/j.rcim.201 2.04.004.
[8] 熊有伦,丁汉,刘恩沧,等.机器人学[M].北京:机械工业出版社,1993.
[9] 李庆,谢一首,郑力新,等.采用Matlab的六自由度机器人三维运动学仿真[J].华侨大学学报(自然科学版),2016,37(3):299-303.DOI:10.11830/ISSN.1000-5013.2016.03.0299.
[10] 蔡自兴.机器人学[M].北京:清华大学出版社,2000.
[11] ABELE E,WEIGOLD M,ROTHENBüCHER S.Modeling and identification of an industrial robot for machining applications[J].CIRP Annals-Manufacturing Technology,2007,56(1):387-390.DOI:10.1016/j.cirp.2007.05.090.
[12] ANGELES J,ROJAS A.Manipulator inverse kinematics via condition-number minimization and continuation[J].International Journal of Robotics and Automation,1987,2(2):61-69.
[13] LIN Yang,ZHAO Huan,DING Han.Posture optimization methodology of 6R industrial robots for machiningusing performance evaluation indexes[J].Robotics and Computer-Integrated Manufacturing,2017,48(12):59-72.DOI:10.1016/j.rcim.2017.02.002.
[14] GOLUB G H,Van LOAN C F.Matrix computations[M].3rd ed.Washington D C:Johns Hopkins University Press,1996.
[15] 侯鹏辉.机器人加工系统刚度性能优化研究[D].杭州:浙江大学,2013.
[16] DUMAS C,CARO S,CHéRIF M,et al.A methodology for joint stiffness identification of serial robots[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Taipei:IEEE Press,2010:464-482.DOI:10.1109/IROS.2010.5652140.
[17] 翁业早.广义逆矩阵理论的研究与应用[D].南京:南京邮电大学,2016.

备注/Memo

备注/Memo:
收稿日期: 2019-05-17
通信作者: 尹方辰(1988-),男,讲师,博士,主要从事机器人加工智能化控制关键技术、先进控制理论在流程工业过程中的应用的研究.E-mail:yfc_ral@163.com.
基金项目: 国家自然科学基金资助项目(U1805251); 教育部创新团队资助项目(IRT-17R41); 华侨大学研究生科研创新基金资助项目(17014080013); 福建省泉州市科技计划项目(2016G048)
更新日期/Last Update: 2019-11-20