Top > IJAT > Visual-Based Joint Compliance Calibration Using Measurement Po ...

single-au.php

IJAT Vol.19 No.3 pp. 204-215
doi: 10.20965/ijat.2025.p0204
(2025)

Research Paper:

Views over last 60 days: 64

Visual-Based Joint Compliance Calibration Using Measurement Pose Optimization

Xiaotian Zhang*1,†, Yusheng Wang*2, Shouhei Shirafuji*3, Naoya Kagawa*4, Noritaka Takamura*4, Keiji Okuhara*4, Hiroyasu Baba*4, and Jun Ota*2

*1Department of Precision Engineering, School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

†Corresponding author

*2Research into Artifacts, Center for Engineering, School of Engineering, The University of Tokyo
Tokyo, Japan

*3Department of Mechanical Engineering, Faculty of Engineering Science, Kansai University
Suita, Japan

*4FA Products Business Unit, Robot Engineering Div. 2, DENSO WAVE Incorporated
Agui, Japan

Received:
November 19, 2024
Accepted:
January 15, 2025
Published:
May 5, 2025
Creative Commons License
Keywords:
robot calibration, perspective-n-point (PnP), observability index, joint compliance, robotics
Abstract

Accurate calibration of robot joint compliance poses a significant challenge with limited existing research. For camera-based calibration, concurrently identifying both joint offsets and compliance errors becomes intricate due to measurement inaccuracies. To overcome this problem, this paper proposes an innovative approach that leverages measurement pose optimization. By leveraging the local product of exponentials (POE) model, our method enables the simultaneous identification of the geometric parameters (joint offsets) and non-geometric parameters (joint compliance). The introduction of a modified visual observability index minimizes sensitivity to camera errors during joint compliance calibration. Experimental results conducted on a 6R serial robot show superior accuracy compared to existing indices, validating the effectiveness of our approach.

Cite this article as:
X. Zhang, Y. Wang, S. Shirafuji, N. Kagawa, N. Takamura, K. Okuhara, H. Baba, and J. Ota, “Visual-Based Joint Compliance Calibration Using Measurement Pose Optimization,” Int. J. Automation Technol., Vol.19 No.3, pp. 204-215, 2025.
Data files:
References
  1. [1] S. Borkar, P. Ghutke, W. Patil, S. Joshi, and S. Sorte, “A review of pick and place robots for the pharmaceutical industry,” 2023 11th Int. Conf. on Emerging Trends in Engineering & Technology-Signal and Information Processing (ICETET-SIP), 2023. https://doi.org/10.1109/ICETET-SIP58143.2023.10151652
  2. [2] R. Riemer and Y. Edan, “Evaluation of influence of target location on robot repeatability,” Robotica, Vol.18, No.4, pp. 443-449, 2000. https://doi.org/10.1017/S0263574799002337
  3. [3] A. Goswami, A. Quaid, and M. Peshkin, “Complete parameter identification of a robot from partial pose information,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 168-173, 1993. https://doi.org/10.1109/ROBOT.1993.291978
  4. [4] Z. Roth, B. Mooring, and B. Ravani, “An overview of robot calibration,” IEEE J. on Robotics and Automation, Vol.3, No.5, pp. 377-385, 1987. https://doi.org/10.1109/JRA.1987.1087124
  5. [5] G. Du, P. Zhang, and D. Li, “Online robot calibration based on hybrid sensors using kalman filters,” Robotics and Computer-Integrated Manufacturing, Vol.31, pp. 91-100, 2015. https://doi.org/10.1016/j.rcim.2014.08.002
  6. [6] G. Du, Y. Liang, C. Li, P. X. Liu, and D. Li, “Online robot kinematic calibration using hybrid filter with multiple sensors,” IEEE Trans. on Instrumentation and Measurement, Vol.69, No.9, pp. 7092-7107, 2020. https://doi.org/10.1109/TIM.2020.2976277
  7. [7] Y. Kong, L. Yang, C. Chen, X. Zhu, D. Li, Q. Guan, and G. Du, “Online kinematic calibration of robot manipulator based on neural network,” Measurement, Vol.238, Article No.115281, 2024. https://doi.org/10.1016/j.measurement.2024.115281
  8. [8] J. H. Jang, S. H. Kim, and Y. K. Kwak, “Calibration of geometric and non-geometric errors of an industrial robot,” Robotica, Vol.19, No.3, pp. 311-321, 2001. https://doi.org/10.1017/S0263574700002976
  9. [9] H. Chen, T. Fuhlbrigge, S. Choi, J. Wang, and X. Li, “Practical industrial robot zero offset calibration,” 2008 IEEE Int. Conf. on Automation Science and Engineering, pp. 516-521, 2008. https://doi.org/10.1109/COASE.2008.4626417
  10. [10] X. Yang, L. Wu, J. Li, and K. Chen, “A minimal kinematic model for serial robot calibration using poe formula,” Robotics and Computer-Integrated Manufacturing, Vol.30, No.3, pp. 326-334, 2014. https://doi.org/10.1016/j.rcim.2013.11.002
  11. [11] Y. Cho, H. M. Do, and J. Cheong, “Screw based kinematic calibration method for robot manipulators with joint compliance using circular point analysis,” Robotics and Computer-Integrated Manufacturing, Vol.60, pp. 63-76, 2019. https://doi.org/10.1016/j.rcim.2018.08.001
  12. [12] C. Gong, J. Yuan, and J. Ni, “Nongeometric error identification and compensation for robotic system by inverse calibration,” Int. J. of Machine Tools and Manufacture, Vol.40, No.14, pp. 2119-2137, 2000. https://doi.org/10.1016/S0890-6955(00)00023-7
  13. [13] G. Duelen and K. Schröer, “Robot calibration—Method and results,” Robotics and Computer-Integrated Manufacturing, Vol.8, No.4, pp. 223-231, 1991. https://doi.org/10.1016/0736-5845(91)90034-P
  14. [14] M. A. Meggiolaro, S. Dubowsky, and C. Mavroidis, “Geometric and elastic error calibration of a high accuracy patient positioning system,” Mechanism and Machine Theory, Vol.40, No.4, pp. 415-427, 2005. https://doi.org/10.1016/j.mechmachtheory.2004.07.013
  15. [15] Y. She, S. Song, H.-J. Su, and J. Wang, “A comparative study on the effect of mechanical compliance for a safe physical human–robot interaction,” J. of Mechanical Design, Vol. 142, No.6, Article No.063305, 2020. https://doi.org/10.1115/1.4046068
  16. [16] M. R. Driels, W. Swayze, and S. Potter, “Full-pose calibration of a robot manipulator using a coordinate-measuring machine,” The Int. J. of Advanced Manufacturing Technology, Vol.8, pp. 34-41, 1993. https://doi.org/10.1007/BF01756635
  17. [17] A. Nubiola and I. A. Bonev, “Absolute calibration of an abb irb 1600 robot using a laser tracker,” Robotics and Computer-Integrated Manufacturing, Vol.29, No.1, pp. 236-245, 2013. https://doi.org/10.1016/j.rcim.2012.06.004
  18. [18] I.-W. Park, B.-J. Lee, S.-H. Cho, Y.-D. Hong, and J.-H. Kim, “Laser-based kinematic calibration of robot manipulator using differential kinematics,” IEEE/ASME Trans. on Mechatronics, Vol.17, No.6, pp. 1059-1067, 2011. https://doi.org/10.1109/TMECH.2011.2158234
  19. [19] H. M. Balanji, A. E. Turgut, and L. T. Tunc, “A novel vision-based calibration framework for industrial robotic manipulators,” Robotics and Computer-Integrated Manufacturing, Vol.73, Article No.102248, 2022. https://doi.org/10.1016/j.rcim.2021.102248
  20. [20] A. Joubair and I. A. Bonev, “Comparison of the efficiency of five observability indices for robot calibration,” Mechanism and Machine Theory, Vol.70, pp. 254-265, 2013. https://doi.org/10.1016/j.mechmachtheory.2013.07.015
  21. [21] D. Daney, Y. Papegay, and B. Madeline, “Choosing measurement poses for robot calibration with the local convergence method and tabu search,” The Int. J. of Robotics Research, Vol.24, No.6, pp. 501-518, 2005. https://doi.org/10.1177/0278364905053185
  22. [22] X. Zhang, H. Goto, S. Shirafuji, K. Okuhara, N. Takamura, N. Kagawa, H. Baba, and J. Ota, “Measurement pose optimization for joint offset calibration with a hand-eye camera,” 2023 9th Int. Conf. on Automation, Robotics and Applications (ICARA), pp. 11-14, 2023. https://doi.org/10.1109/ICARA56516.2023.10125953
  23. [23] S. Shirafuji, H. Goto, X. Zhang, K. Okuhara, N. Takamura, N. Kagawa, H. Baba, and J. Ota, “Visual-biased observability index for camera-based robot calibration,” J. of Mechanisms and Robotics, Vol.16, No.5, Article No.051010, 2024. https://doi.org/10.1115/1.4062801
  24. [24] J.-H. Borm and C.-H. Meng, “Determination of optimal measurement configurations for robot calibration based on observability measure,” The Int. J. of Robotics Research, Vol.10, No.1, pp. 51-63, 1991. https://doi.org/10.1177/027836499101000106
  25. [25] Y. Sun and J. M. Hollerbach, “Observability index selection for robot calibration,” 2008 IEEE Int. Conf. on Robotics and Automation, pp. 831-836, 2008. https://doi.org/10.1109/ROBOT.2008.4543308
  26. [26] M. R. Driels and U. S. Pathre, “Significance of observation strategy on the design of robot calibration experiments,” J. of Robotic Systems, Vol.7, No.2, pp. 197-223, 1990. https://doi.org/10.1002/rob.4620070206
  27. [27] A. Nahvi, J. M. Hollerbach, and V. Hayward, “Calibration of a parallel robot using multiple kinematic closed loops,” Proc. of the 1994 IEEE Int. Conf. on robotics and automation, pp. 407-412, 1994. https://doi.org/10.1109/ROBOT.1994.351262
  28. [28] A. Nahvi and J. M. Hollerbach, “The noise amplification index for optimal pose selection in robot calibration,” Proc. of IEEE Int. Conf. on Robotics and Automation, Vol.1, pp. 647-654, 1996. https://doi.org/10.1109/ROBOT.1996.503848
  29. [29] A. Joubair, A. Tahan, and I. A. Bonev, “Performances of observability indices for industrial robot calibration,” 2016 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 2477-2484, 2016. https://doi.org/10.1109/IROS.2016.7759386
  30. [30] S. Hayati, K. Tso, and G. Roston, “Robot geometry calibration,” Proc. 1988 IEEE Int. Conf. on Robotics and Automation, pp. 947-951, 1988. https://doi.org/10.1109/ROBOT.1988.12181
  31. [31] I.-C. Ha, “Kinematic parameter calibration method for industrial robot manipulator using the relative position,” J. of Mechanical Science and Technology, Vol.22, No.6, pp. 1084-1090, 2008. https://doi.org/10.1007/s12206-008-0305-0
  32. [32] G. Chen, H. Wang, and Z. Lin, “Determination of the identifiable parameters in robot calibration based on the poe formula,” IEEE Trans. on Robotics, Vol.30, No.5, pp. 1066-1077, 2014. https://doi.org/10.1109/TRO.2014.2319560
  33. [33] F. Chaumette and S. Hutchinson, “Visual servo control. i. basic approaches,” IEEE Robotics & Automation Magazine, Vol.13, No.4, pp. 82-90, 2006. https://doi.org/10.1109/MRA.2006.250573
  34. [34] T. J. Mitchell and C. K. Bayne, “D-optimal fractions of three-level factorial designs,” Technometrics, Vol.20, No.4, pp. 369-380, 1978. https://doi.org/10.1080/00401706.1978.10489689
  35. [35] T. J. Mitchell, “An algorithm for the construction of “d-optimal” experimental designs,” Technometrics, Vol.42, No.1, pp. 48-54, 2000. https://doi.org/10.1080/00401706.2000.10485978
  36. [36] S. C. Endres, C. Sandrock, and W. W. Focke, “A simplicial homology algorithm for lipschitz optimisation,” J. of Global Optimization, Vol.72, pp. 181-217, 2018. https://doi.org/10.1007/s10898-018-0645-y

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 30, 2025