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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 Jiang Open the ORCID record for Zhouxiang Jiang [Opens in a new window]  and
Min Huang Open 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, Beijing100192, China, E-mail: [email protected] Key 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, Beijing100192, China, E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

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
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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References

Elatta, A. Y., Li, P. G., Fan, L. Z. and Yu, D., “An overview of robot calibration,” Inf. Technol. J. 3(1), 74–78 (2004).CrossRefGoogle Scholar
Gang, C., Tong, L., Ming, C., Xuan, J. Q. and Xu, S. H., “Review on kinematics calibration technology of serial robots,” Int. J. Precision Eng. Manuf. 15(8), 1759–1774 (2014). https://doi.org/10.1007/s12541-014-0528-1.CrossRefGoogle Scholar
Ma, L., Bazzoli, P. and Sammons, P. M., “Modeling and calibration of high-order joint-dependent kinematic errors for industrial robots,” Rob. Comput. Integr. Manuf. 50, 153–167 (2018). https://doi.org/10.1016/j.rcim.2017.09.006.CrossRefGoogle Scholar
Gong, C. H., Yuan, J. X and Ni, J., “Nongeometric error identification and compensation for robotic system by inverse calibration,” Int. J. Mach. Tools Manuf. 40(14), 2119–2137 (2000). https://doi.org/10.1016/s0890-69550000023-7.CrossRefGoogle Scholar
Roth, Z., Mooring, B. and Ravani, B., “An overview of robot calibration,” IEEE J. Rob. Autom. 3(5), 377–385 (1987). https://doi.org/10.1109/jra.1987.1087124.CrossRefGoogle Scholar
Nubiola, A. and Bonev, I. A., “Absolute calibration of an ABB IRB 1600 robot using a laser tracker,” Rob. Comput. Integr. Manuf. 29(1), 236–245 (2013). https://doi.org/10.1016/j.rcim.2012.06.004.CrossRefGoogle Scholar
Wu, Y., Klimchik, A., Caro, S., Furet, B. and Pashkevich, A., “Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments,” Rob. Comput. Integr. Manuf. 35, 151–168 (2015). https://doi.org/10.1016/j.rcim.2015.03.007.CrossRefGoogle Scholar
Li, C., Wu, Y. Q. and Lowe, H., “POE-based robot kinematic calibration using axis configuration space and the adjoint error model,” IEEE Trans. Rob. 32(5), 1264–1279 (2016). https://doi.org/10.1109/tro.2016.2593042.CrossRefGoogle Scholar
Gao, G. B., Sun, G. Q., Na, J., Guo, Y. and Wu, X., “Structural parameter identification for 6 DOF industrial robots,” Mech. Syst. Signal Process. (2017). https://doi.org/10.1016/j.ymssp.2017.08.011.Google Scholar
Chen, X. Y., Zhang, Q. J. and Sun, Y. L., “Non-kinematic calibration of industrial robots using a rigid-flexible coupling error model and a full pose measurement method,” Rob. Comput. Integr. Manuf. 57, 46–58 (2019). https://doi.org/10.1016/j.rcim.2018.07.002.CrossRefGoogle Scholar
Qiao, Y., Chen, Y. P. and Chen, B., “A novel calibration method for multi-robots system utilizing calibration model without nominal kinematic parameters,” Precision Eng. 50, 211–221 (2017). https://doi.org/10.1016/j.precisioneng.2017.05.009.CrossRefGoogle Scholar
Nubiola, A. and Bonev, I. A., “Absolute robot calibration with a single telescoping ballbar,” Precision Eng. 38(3), 472–480 (2014). https://doi.org/10.1016/j.precisioneng.2014.01.001.CrossRefGoogle Scholar
Nubiola, A. and Bonev, I. A., “Non-kinematic calibration of a six-axis serial robot using planar constraints,” Precision Eng. 40(3), 472–480 (2015). https://doi.org/10.1016/j.precisioneng.2014.12.002.Google Scholar
Cai, Y. Y., Gu, H., Li, C. and Liu, H. S., “Easy industrial robot cell coordinates calibration with touch panel,” Rob. Comput. Integr. Manuf. 50, 276–285 (2018). https://doi.org/10.1016/j.rcim.2017.10.004.CrossRefGoogle Scholar
Wang, W., Liu, F. and Yun, C., “Calibration method of robot base frame using unit quaternion form,” Precision Eng. 41, 47–54 (2015). https://doi.org/10.1016/j.precisioneng.2015.01.005.CrossRefGoogle Scholar
Radhe, S. S., Santosh, S., Laxmidhar, B. and Venkatesh, K. S., “Position-based visual servoing of a mobile robot with an automatic extrinsic calibration scheme,” Robotica 38(5), 831–844 (2020). https://doi.org/10.1017/S0263574719001115.Google Scholar
Borm, J. H. and Menq, C. H., “Determination of optimal measurement configurations for robot calibration based on observability measure,” Int. J. Rob. Res. 10(1), 51–63 (1991). https://doi.org/10.1177/027836499101000106.CrossRefGoogle Scholar
Fu, Z. T., Dai, J. S., Yang, K., Chen, X. B. and López-Custodio, P., “Analysis of unified error model and simulated parameters calibration for robotic machining based on Lie theory,” Rob. Comput. Integr. Manuf. 61, 1–14 (2020). https://doi.org/10.1016/j.rcim.2019.101855.CrossRefGoogle Scholar
Joubair, A., Tahan, A. S. and Bonev, I. A., “Performances of Observability Indices for Industrial Robot Calibration,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016) pp. 2477–2484. https://doi.org/10.1109/iros.2016.7759386.Google Scholar
Elie, S. and Shadi, N., “Kinematic calibration of serial manipulators using Bayesian inference,” Robotica 36(5), 738–766 (2018). https://doi.org/10.1017/S0263574718000024.Google Scholar
Genliang, C., Lingyu, K., Qinchuan, L. and Hao, W., A simple two-step geometric approach for the kinematic calibration of the 3-PRS parallel manipulator,” Robotica. 37(5), 837–850 (2019). https://doi.org/10.1017/S0263574718001352.Google Scholar
Chenguang, C., Jinguo, L., Zhiyu, N. and Ruolong, Q., “An improved kinematic calibration method for serial manipulators based on POE formula,” Robotica 36(8), 1244–1262 (2018). https://doi.org/10.1017/S0263574718000280.CrossRefGoogle Scholar
Klimchik, A., Furet, B., S. Caro and A. Pashkevich “Identification of the manipulator stiffness model parameters inindustrial environment,” Mech. Mach. Theory 90, 1–22 (2015). https://doi.org/10.1016/j.mechmachtheory.2015.03.002.CrossRefGoogle Scholar
Kamali, K. and Bonev, I. A., “Optimal experiment design for elasto-geometrical calibration of industrial robots,” IEEE/ASME Trans. Mechatron. 24(6), 2733–2744 (2019). https://doi.org/10.1109/tmech.2019.2944428.CrossRefGoogle Scholar
Theissen, N. A., Laspas, T. and Archenti, A., “Closed-force-loop elastostatic calibration of serial articulated robots,” Rob. Comput. Integr. Manuf. 57, 86–91 (2019). https://doi.org/10.1016/j.rcim.2018.07.007.CrossRefGoogle Scholar
Huang, T., Zhao, D., Yin, F. W., Tian, W. J. and Chetwynd, D. G., “Kinematic calibration of a 6-DOF hybrid robot by considering multicollinearity in the identification Jacobian,” Mech. Mach. Theory 131, 371–384 (2019). https://doi.org/10.1016/j.mechmachtheory.2018.10.008.CrossRefGoogle Scholar
Nahvi, A. and Hollerbach, J. M., “The Noise Amplification Index for Optimal Pose Selection in Robot Calibration,” 1996 IEEE International Conference on Robotics and Automation, vol. 1(1) (1996) pp. 647–654. https://doi.org/10.1109/robot.1996.503848.Google Scholar
Mitchell, T. J., “An algorithm for the construction of ‘D-Optimal’ experimental designs,” Technometrics 42(1), 48–54 (2000). https://doi.org/10.1080/00401706.2000.10485978.Google Scholar
Sklar, M. E., “Geometric Calibration of Industrial Manipulators by Circle Point Analysis,” Proceedings of the 2nd Conference on Recent Advances in Robotics (1989) pp. 178–202.Google Scholar
Santolaria, J., Conte, J., Pueo, M. and Javierre, C., “Rotation error modeling and identification for robot kinematic calibration by circle point method,” Metrol. Meas. Syst. 21(1), 85–98 (2014). https://doi.org/10.2478/mms-2014-0009.CrossRefGoogle Scholar
Cho, Y. S., Do, H. M. and Cheong, J. N., “Screw based kinematic calibration method for robot manipulators with joint compliance using circular point analysis,” Rob. Comput. Integr. Manuf. (2018). https://doi.org/10.1016/j.rcim.2018.08.001.Google Scholar