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An accurate identification method based on double weighting for inertial parameters of robot payloads

Published online by Cambridge University Press:  15 July 2022

Tian Xu Open the ORCID record for Tian Xu [Opens in a new window] ,
Jizhuang Fan ,
Qianqian Fang Open the ORCID record for Qianqian Fang [Opens in a new window] ,
Yanhe Zhu  and
Jie Zhao
Tian Xu*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Jizhuang Fan*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Qianqian Fang
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Yanhe Zhu
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Jie Zhao
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
*
*Corresponding author. E-mail: xutian_hit@163.com; fanjizhuang@hit.edu.cn
*Corresponding author. E-mail: xutian_hit@163.com; fanjizhuang@hit.edu.cn
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Abstract

The inertial parameters of payloads attached to the end effector of robots benefit to several robotics applications, such as the model-based control, the task optimization, and so on. These applications require the accurate estimation of the inertial parameters. In the existing payload estimation approaches, however, the data weighting technique, which can reduce the adverse effects of outliers and significantly improve the final results, has not been applied yet. In this article, an accurate identification method based on double weighting for inertial parameters of robot payloads is proposed. In order to obtain the weighting matrices, a modified dynamic parameter identification method with two loops is firstly proposed. Then, based on the identified results of dynamic parameters, a payload identification model based on double weighting is constructed. In addition, the variations of both nonlinear friction parameters and linear friction parameters caused by the payload are considered in this model. Finally, experimental comparisons between our method and another four methods are conducted. The results confirm that our method shows the best performance, especially on improving the identification accuracy of mass and center of mass of the payload.

Keywords

robot payload identificationdouble weightingdynamic parameter identificationnonlinear friction modelUR10 robot

Information

Type
Research Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4358 - 4374
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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