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Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation

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

Measurement, Volume: 155, Start page: 107524

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.measurement.2020.107524

Abstract

Kinematic calibration has been widely employed for manipulators to promote their performance characteristics. For a dual-arm manipulator, most of the research attentions are paid to improving the absolute positioning accuracy. However, collaborative accuracy plays a critical role in mutual operation...

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Published in: Measurement
ISSN: 0263-2241
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53485
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spelling 2020-02-10T13:05:08.2712196 v2 53485 2020-02-10 Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-02-10 MECH Kinematic calibration has been widely employed for manipulators to promote their performance characteristics. For a dual-arm manipulator, most of the research attentions are paid to improving the absolute positioning accuracy. However, collaborative accuracy plays a critical role in mutual operations between the two arms. For example, in dangerous chemical experiments, the dual-arm manipulator is often demanded to grab a target object with the two hands, or re-grasp a test tube from one hand to the other, where the inferior collaborative accuracy may lead to the failure of experiments. Hence, in this paper, collaborative accuracy of dual-arm manipulators is well defined and fully considered as the objective for calibration. Robustness of the calibration is further guaranteed by minimizing the maximum distance error. The formulated problem is not a typical convex optimization, and gradient search algorithm does not work well for this problem. With researches on optimization moving forward, recent advances in nonlinear optimization are employed to seek for the solution effectively, and it is found that the minimax problem can be approximately linearized to a sequence quadratic programming (SQP) problem. Furthermore, a primal-dual subgradient algorithm is applied for solving the SQP problem with a fast local convergence. Finally, in order to verify the superiority of the proposed method, an experiment is performed on an IRB 14000 manipulator, and corresponding outcomes indicate that the RMS collaborative positioning and the orientation accuracies are significantly improved by and . To the best of our knowledge, our method has reached the best collaborative accuracy compared with existing works (Wang et al., 2014; Roncone et al., 2014; Motta et al., 2001). Journal Article Measurement 155 107524 Elsevier BV 0263-2241 Dual-arm manipulators, Kinematic calibration, Robustness, Collaborative accuracy, Relative accuracy 1 4 2020 2020-04-01 10.1016/j.measurement.2020.107524 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-02-10T13:05:08.2712196 2020-02-10T13:05:08.2712196 Chentao Mao 1 Shuai Li 0000-0001-8316-5289 2 Zhangwei Chen 3 Xiang Zhang 4 Chao Li 5 53485__16558__31cb9d7057d94f5db0e41210d8a0b5ad.pdf mao2020.pdf 2020-02-11T09:18:54.0693500 Output 1830299 application/pdf Accepted Manuscript true 2021-01-23T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation
spellingShingle Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation
Shuai Li
title_short Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation
title_full Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation
title_fullStr Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation
title_full_unstemmed Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation
title_sort Robust kinematic calibration for improving collaboration accuracy of dual-arm manipulators with experimental validation
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Chentao Mao
Shuai Li
Zhangwei Chen
Xiang Zhang
Chao Li
format Journal article
container_title Measurement
container_volume 155
container_start_page 107524
publishDate 2020
institution Swansea University
issn 0263-2241
doi_str_mv 10.1016/j.measurement.2020.107524
publisher Elsevier BV
document_store_str 1
active_str 0
description Kinematic calibration has been widely employed for manipulators to promote their performance characteristics. For a dual-arm manipulator, most of the research attentions are paid to improving the absolute positioning accuracy. However, collaborative accuracy plays a critical role in mutual operations between the two arms. For example, in dangerous chemical experiments, the dual-arm manipulator is often demanded to grab a target object with the two hands, or re-grasp a test tube from one hand to the other, where the inferior collaborative accuracy may lead to the failure of experiments. Hence, in this paper, collaborative accuracy of dual-arm manipulators is well defined and fully considered as the objective for calibration. Robustness of the calibration is further guaranteed by minimizing the maximum distance error. The formulated problem is not a typical convex optimization, and gradient search algorithm does not work well for this problem. With researches on optimization moving forward, recent advances in nonlinear optimization are employed to seek for the solution effectively, and it is found that the minimax problem can be approximately linearized to a sequence quadratic programming (SQP) problem. Furthermore, a primal-dual subgradient algorithm is applied for solving the SQP problem with a fast local convergence. Finally, in order to verify the superiority of the proposed method, an experiment is performed on an IRB 14000 manipulator, and corresponding outcomes indicate that the RMS collaborative positioning and the orientation accuracies are significantly improved by and . To the best of our knowledge, our method has reached the best collaborative accuracy compared with existing works (Wang et al., 2014; Roncone et al., 2014; Motta et al., 2001).
published_date 2020-04-01T04:06:26Z
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score 11.036531

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