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An improved iterative approach with a comprehensive friction model for identifying dynamic parameters of collaborative robots

Published online by Cambridge University Press:  20 March 2024

Zeyu Li Open the ORCID record for Zeyu Li [Opens in a new window] ,
Hongxing Wei ,
Chengguo Liu ,
Ye He ,
Gang Liu ,
Haochen Zhang  and
Weiming Li
Zeyu Li
Affiliation:
School of Mechanical Engineering & Automation, Beihang University, Beijing, China
Hongxing Wei*
Affiliation:
School of Mechanical Engineering & Automation, Beihang University, Beijing, China
Chengguo Liu*
Affiliation:
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
Ye He
Affiliation:
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
Gang Liu
Affiliation:
School of Mechanical Engineering & Automation, Beihang University, Beijing, China
Haochen Zhang
Affiliation:
School of Mechanical Engineering & Automation, Beihang University, Beijing, China
Weiming Li
Affiliation:
School of Mechanical Engineering & Automation, Beihang University, Beijing, China
*
Corresponding authors: Chengguo Liu; Email: 13297915920@163.com, Hongxing Wei; Email: weihongxing@buaa.edu.cn
Corresponding authors: Chengguo Liu; Email: 13297915920@163.com, Hongxing Wei; Email: weihongxing@buaa.edu.cn
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Abstract

Collaborative robots are becoming intelligent assistants of human in industrial settings and daily lives. Dynamic model identification is an active topic for collaborative robots because it can provide effective ways to achieve precise control, fast collision detection and smooth lead-through programming. In this research, an improved iterative approach with a comprehensive friction model for dynamic model identification is proposed for collaborative robots when the joint velocity, temperature and load torque effects are considered. Experiments are conducted on the AUBO I5 collaborative robots. Two other existing identification algorithms are adopted to make comparison with the proposed approach. It is verified that the average error of the proposed I-IRLS algorithm is reduced by over 14% than that of the classical IRLS algorithm. The proposed I-IRLS method can be widely used in various application scenarios of collaborative robots.

Keywords

collaborative robotsmodel parameters identificationweighted least-squaresconvex optimizationfriction
Type
Research Article
Information
Robotica , First View , pp. 1 - 23
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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