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Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots

Chentao Mao, Zhangwei Chen, Shuai Li Orcid Logo, Xiang Zhang

Journal of Intelligent & Robotic Systems, Volume: 101, Issue: 1

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1007/s10846-020-01268-z

Abstract

Kinematic calibration of robots is an effective way to guarantee and promote their performance characteristics. There are many mature researches on kinematic calibration, and methods based on MDH model are the most common ones. However, when employing these calibration methods, it occasionally happe...

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Published in: Journal of Intelligent & Robotic Systems
ISSN: 0921-0296 1573-0409
Published: Springer Science and Business Media LLC 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa55908
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first_indexed 2020-12-17T09:43:12Z
last_indexed 2021-05-05T03:21:37Z
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spelling 2021-05-04T09:59:43.4102119 v2 55908 2020-12-17 Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-12-17 MECH Kinematic calibration of robots is an effective way to guarantee and promote their performance characteristics. There are many mature researches on kinematic calibration, and methods based on MDH model are the most common ones. However, when employing these calibration methods, it occasionally happens that the objective function cannot converge during iterations. Through analyzing robotic forward kinematics, we found out that the Cartesian coordinates of the end-point are affine to length-related MDH parameters, where linear and nonlinear parameters can be separated. Thanks to the distinctive characteristic of the MDH model, the kinematic calibration problem can be converted into a separable nonlinear least squares problem, which can further be partitioned into two subproblems: a linear least squares problem and a reduced problem involving only nonlinear parameters. Eventually, the optimal structural parameters can be identified by solving this problem iteratively. The results of numerical and experimental validations show that: 1) the robustness during identification procedure is enhanced by eliminating the partial linear structural parameters, the convergence rate is promoted from 68.98% to 100% with different deviation vector pairs; 2) the initial values to be pre-set for kinematic calibration problem are fewer and 3) fewer parameters are to be identified by nonlinear least squares regression, resulting in fewer iterations and faster convergence, where average runtime is reduced from 33.931s to 1.874s. Journal Article Journal of Intelligent & Robotic Systems 101 1 Springer Science and Business Media LLC 0921-0296 1573-0409 Kinematic calibration; Robustness; Separable nonlinear least squares; Positioning accuracy 8 12 2020 2020-12-08 10.1007/s10846-020-01268-z COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-05-04T09:59:43.4102119 2020-12-17T09:39:19.8534240 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Chentao Mao 1 Zhangwei Chen 2 Shuai Li 0000-0001-8316-5289 3 Xiang Zhang 4
title Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
spellingShingle Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
Shuai Li
title_short Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
title_full Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
title_fullStr Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
title_full_unstemmed Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
title_sort Separable Nonlinear Least Squares Algorithm for Robust Kinematic Calibration of Serial Robots
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Chentao Mao
Zhangwei Chen
Shuai Li
Xiang Zhang
format Journal article
container_title Journal of Intelligent & Robotic Systems
container_volume 101
container_issue 1
publishDate 2020
institution Swansea University
issn 0921-0296
1573-0409
doi_str_mv 10.1007/s10846-020-01268-z
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 0
active_str 0
description Kinematic calibration of robots is an effective way to guarantee and promote their performance characteristics. There are many mature researches on kinematic calibration, and methods based on MDH model are the most common ones. However, when employing these calibration methods, it occasionally happens that the objective function cannot converge during iterations. Through analyzing robotic forward kinematics, we found out that the Cartesian coordinates of the end-point are affine to length-related MDH parameters, where linear and nonlinear parameters can be separated. Thanks to the distinctive characteristic of the MDH model, the kinematic calibration problem can be converted into a separable nonlinear least squares problem, which can further be partitioned into two subproblems: a linear least squares problem and a reduced problem involving only nonlinear parameters. Eventually, the optimal structural parameters can be identified by solving this problem iteratively. The results of numerical and experimental validations show that: 1) the robustness during identification procedure is enhanced by eliminating the partial linear structural parameters, the convergence rate is promoted from 68.98% to 100% with different deviation vector pairs; 2) the initial values to be pre-set for kinematic calibration problem are fewer and 3) fewer parameters are to be identified by nonlinear least squares regression, resulting in fewer iterations and faster convergence, where average runtime is reduced from 33.931s to 1.874s.
published_date 2020-12-08T04:10:28Z
_version_ 1763753725302669312
score 11.036334

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