Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix

Xingguang Duan; Lixing Jin; Changsheng Li; Rui He; Quanbin Lai; Rui Ma

doi:10.1109/RCAR52367.2021.9517431

Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix

Xingguang Duan, Lixing Jin^*, Changsheng Li, Rui He, Quanbin Lai, Rui Ma

^*Corresponding author for this work

School of Mechatronical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Kinematics calibration is an effective means to improve the accuracy of the mechanism. Parallel mechanisms with redundant actuation have potential for kinematic self-calibration. In this paper, the self-calibration algorithms of a parallel mechanism with 3-DOF are studied. The Jacobian matrix for self-calibration is ill-posed/ill-condition caused by multicollinearity between kinematics parameter, and the calculation with least square method diverges. Truncated singular value decomposition (TSVD), ridge regression (RR) and Liu estimation algorithm are utilized to address this problem, and the identification performances under different error parameters are compared.

Original language	English
Title of host publication	2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	314-318
Number of pages	5
ISBN (Electronic)	9781665436786
DOIs	https://doi.org/10.1109/RCAR52367.2021.9517431
Publication status	Published - 15 Jul 2021
Event	2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021 - Xining, China Duration: 15 Jul 2021 → 19 Jul 2021

Publication series

Name	2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

Conference

Conference	2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021
Country/Territory	China
City	Xining
Period	15/07/21 → 19/07/21

Access to Document

10.1109/RCAR52367.2021.9517431

Cite this

Duan, X., Jin, L., Li, C., He, R., Lai, Q., & Ma, R. (2021). Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix. In 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021 (pp. 314-318). (2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RCAR52367.2021.9517431

Duan, Xingguang ; Jin, Lixing ; Li, Changsheng et al. / Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix. 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 314-318 (2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021).

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title = "Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix",

abstract = "Kinematics calibration is an effective means to improve the accuracy of the mechanism. Parallel mechanisms with redundant actuation have potential for kinematic self-calibration. In this paper, the self-calibration algorithms of a parallel mechanism with 3-DOF are studied. The Jacobian matrix for self-calibration is ill-posed/ill-condition caused by multicollinearity between kinematics parameter, and the calculation with least square method diverges. Truncated singular value decomposition (TSVD), ridge regression (RR) and Liu estimation algorithm are utilized to address this problem, and the identification performances under different error parameters are compared.",

author = "Xingguang Duan and Lixing Jin and Changsheng Li and Rui He and Quanbin Lai and Rui Ma",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021 ; Conference date: 15-07-2021 Through 19-07-2021",

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doi = "10.1109/RCAR52367.2021.9517431",

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series = "2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021",

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Duan, X, Jin, L, Li, C, He, R, Lai, Q & Ma, R 2021, Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix. in 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021. 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021, Institute of Electrical and Electronics Engineers Inc., pp. 314-318, 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021, Xining, China, 15/07/21. https://doi.org/10.1109/RCAR52367.2021.9517431

Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix. / Duan, Xingguang; Jin, Lixing; Li, Changsheng et al.
2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 314-318 (2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix

AU - Duan, Xingguang

AU - Jin, Lixing

AU - Li, Changsheng

AU - He, Rui

AU - Lai, Quanbin

AU - Ma, Rui

PY - 2021/7/15

Y1 - 2021/7/15

N2 - Kinematics calibration is an effective means to improve the accuracy of the mechanism. Parallel mechanisms with redundant actuation have potential for kinematic self-calibration. In this paper, the self-calibration algorithms of a parallel mechanism with 3-DOF are studied. The Jacobian matrix for self-calibration is ill-posed/ill-condition caused by multicollinearity between kinematics parameter, and the calculation with least square method diverges. Truncated singular value decomposition (TSVD), ridge regression (RR) and Liu estimation algorithm are utilized to address this problem, and the identification performances under different error parameters are compared.

AB - Kinematics calibration is an effective means to improve the accuracy of the mechanism. Parallel mechanisms with redundant actuation have potential for kinematic self-calibration. In this paper, the self-calibration algorithms of a parallel mechanism with 3-DOF are studied. The Jacobian matrix for self-calibration is ill-posed/ill-condition caused by multicollinearity between kinematics parameter, and the calculation with least square method diverges. Truncated singular value decomposition (TSVD), ridge regression (RR) and Liu estimation algorithm are utilized to address this problem, and the identification performances under different error parameters are compared.

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T3 - 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

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BT - 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

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Duan X, Jin L, Li C, He R, Lai Q, Ma R. Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix. In 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 314-318. (2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021). doi: 10.1109/RCAR52367.2021.9517431

Kinematic self-calibration of a 3-DOF parallel mechanism with ill-conditioned identification matrix

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