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Stable Calibrations of Six-DOF Serial Robots by Using Identification Models with Equalized Singular Values

Published online by Cambridge University Press:  15 March 2021

Zhouxiang JiangOpen the ORCID record for Zhouxiang Jiang[Opens in a new window] and
Min HuangOpen the ORCID record for Min Huang[Opens in a new window]
Zhouxiang Jiang*
Affiliation:
Institute of Electromechanical Engineering, Beijing Information Science & Technology University, 12 Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, China, E-mail: huangmin@bistu.edu.cnKey Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing, China
Min Huang
Affiliation:
Institute of Electromechanical Engineering, Beijing Information Science & Technology University, 12 Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, China, E-mail: huangmin@bistu.edu.cn
*
*Corresponding author. E-mail: jiangzhouxiang@bistu.edu.cn
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Summary

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In typical calibration methods (kinematic or non-kinematic) for serial industrial robot, though measurement instruments with high resolutions are adopted, measurement configurations are optimized, and redundant parameters are eliminated from identification model, calibration accuracy is still limited under measurement noise. This might be because huge gaps still exist among the singular values of typical identification Jacobians, thereby causing the identification models ill conditioned. This paper addresses such problem by using new identification models established in two steps. First, the typical models are divided into the submodels with truncated singular values. In this way, the unknown parameters corresponding to the abnormal singular values are removed, thereby reducing the condition numbers of the new submodels. However, these models might still be ill conditioned. Therefore, the second step is to further centralize the singular values of each submodel by using a matrix balance method. Afterward, all submodels are well conditioned and obtain much higher observability indices compared with those of typical models. Simulation results indicate that significant improvements in the stability of identification results and the identifiability of unknown parameters are acquired by using the new identification submodels. Experimental results indicate that the proposed calibration method increases the identification accuracy without incurring additional hardware setup costs to the typical calibration method.

Keywords

Industrial robotKinematic calibrationNon-kinematic calibrationTruncated singular valuesMatrix balance

Information

Type
Article
Information
Robotica , Volume 39 , Issue 12 , December 2021, pp. 2131 - 2152
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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