Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot
To improve the absolute positional accuracy of the serial six-DOF robot， the joint stiffness error of industrial robots after geometric parameter error compensation is studied. Firstly，the one-dimensional joint stiffness error model of industrial robots is established based on the virtual joint mode...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2019-06-01
|
| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.06.007 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548811318591488 |
|---|---|
| author | Ping Rui Guifang Qiao Xiulan Wen Ying Zhang Dongxia Wang |
| author_facet | Ping Rui Guifang Qiao Xiulan Wen Ying Zhang Dongxia Wang |
| author_sort | Ping Rui |
| collection | DOAJ |
| description | To improve the absolute positional accuracy of the serial six-DOF robot， the joint stiffness error of industrial robots after geometric parameter error compensation is studied. Firstly，the one-dimensional joint stiffness error model of industrial robots is established based on the virtual joint model. Secondly，in order to improve the identification accuracy and efficiency of joint stiffness parameters, the BP neural network is applied to fit the stiffness error model to optimize the initial population fitness of genetic algorithm. Finally，the laser tracker AT930 and ER10L-C10 robot are used to verify the above error model and joint stiffness parameter identification algorithm. The experimental results show that the average distance error and maximum distance error of the robot are 0.248 5 mm and 0.333 2 mm respectively after the joint stiffness error compensation. Compared with the distance error before error compensation，the positional accuracy of robot is improved by 33.7%. Therefore，through the proposed improved genetic algorithm can identify the joint stiffness parameters accurately and improve the positional accuracy effectively. |
| format | Article |
| id | doaj-art-1f42eca4e3a248428d62a211999def0a |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2019-06-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-1f42eca4e3a248428d62a211999def0a2025-01-10T14:00:21ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392019-06-0143374230641224Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF RobotPing RuiGuifang QiaoXiulan WenYing ZhangDongxia WangTo improve the absolute positional accuracy of the serial six-DOF robot， the joint stiffness error of industrial robots after geometric parameter error compensation is studied. Firstly，the one-dimensional joint stiffness error model of industrial robots is established based on the virtual joint model. Secondly，in order to improve the identification accuracy and efficiency of joint stiffness parameters, the BP neural network is applied to fit the stiffness error model to optimize the initial population fitness of genetic algorithm. Finally，the laser tracker AT930 and ER10L-C10 robot are used to verify the above error model and joint stiffness parameter identification algorithm. The experimental results show that the average distance error and maximum distance error of the robot are 0.248 5 mm and 0.333 2 mm respectively after the joint stiffness error compensation. Compared with the distance error before error compensation，the positional accuracy of robot is improved by 33.7%. Therefore，through the proposed improved genetic algorithm can identify the joint stiffness parameters accurately and improve the positional accuracy effectively.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.06.007Industrial robotParameter calibrationJoint stiffnessError compensationGenetic algorithm |
| spellingShingle | Ping Rui Guifang Qiao Xiulan Wen Ying Zhang Dongxia Wang Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot Jixie chuandong Industrial robot Parameter calibration Joint stiffness Error compensation Genetic algorithm |
| title | Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot |
| title_full | Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot |
| title_fullStr | Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot |
| title_full_unstemmed | Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot |
| title_short | Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot |
| title_sort | research on joint stiffness identification and error compensation of the serial six dof robot |
| topic | Industrial robot Parameter calibration Joint stiffness Error compensation Genetic algorithm |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.06.007 |
| work_keys_str_mv | AT pingrui researchonjointstiffnessidentificationanderrorcompensationoftheserialsixdofrobot AT guifangqiao researchonjointstiffnessidentificationanderrorcompensationoftheserialsixdofrobot AT xiulanwen researchonjointstiffnessidentificationanderrorcompensationoftheserialsixdofrobot AT yingzhang researchonjointstiffnessidentificationanderrorcompensationoftheserialsixdofrobot AT dongxiawang researchonjointstiffnessidentificationanderrorcompensationoftheserialsixdofrobot |