Practically Identifiable Model of Robotic Manipulator for Calibration in Real Industrial Environment

  • Alexandr Klimchik
  • Stephane Caro
  • Benoit Furet
  • Anatol Pashkevich
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

The paper addresses a problem of robotic manipulator calibration in real industrial environment. Particular attention is paid to the practical identifiability of the model parameters, which completely differs from the theoretical one that relies on the rank of the observation matrix only, without taking into account essential differences in the model parameter magnitudes and the measurement noise impact. To solve the problem, several model reduction methods are proposed. The advantages of the developed approach are illustrated by an application example that deals with the geometric calibration of an industrial robot used in aerospace industry.

Keywords

Robotic manipulator Geometric calibration Parameter identifiability Model reduction Experimental validation 

Notes

Acknowledgments

The work presented in this paper was partially funded by ANR (Project ANR-2010-SEGI-003-02-COROUSSO) and FEDER ROBOTEX, France.

References

  1. 1.
    Hollerbach, J., W. Khalil and M. Gautier (2008). Model Identification. Springer Handbook of Robotics. B. Siciliano and O. Khatib, Springer, Berlin Heidelberg: 321–344.Google Scholar
  2. 2.
    Elatta, A., L. P. Gen, F. L. Zhi, Y. Daoyuan and L. Fei (2004). “An overview of robot calibration.” Information Technology Journal 3(1): 74–78.Google Scholar
  3. 3.
    Stone, H. W. (1987). Kinematic modeling, identification, and control of robotic manipulators, Springer.Google Scholar
  4. 4.
    Nubiola, A. and I. A. Bonev (2013). “Absolute calibration of an ABB IRB 1600 robot using a laser tracker.” Robotics and Computer-Integrated Manufacturing 29(1): 236–245.Google Scholar
  5. 5.
    Liu, Y., Z. Jiang, H. Liu and W. Xu (2012). “Geometric Parameter Identification of a 6-DOF Space Robot Using a Laser-Ranger.” Journal of Robotics 2012.Google Scholar
  6. 6.
    Goswami, A., A. Quaid and M. Peshkin (1993). Complete parameter identification of a robot from partial pose information. Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on, IEEE.Google Scholar
  7. 7.
    Klimchik, A., Y. Wu, S. Caro, B. Furet and A. Pashkevich (2013). Advanced robot calibration using partial pose measurements. Methods and Models in Automation and Robotics (MMAR), 2013 18th International Conference on, IEEE.Google Scholar
  8. 8.
    Takeda, Y., G. Shen and H. Funabashi (2004). “A DBB-based kinematic calibration method for in-parallel actuated mechanisms using a Fourier series.” Journal of Mechanical Design 126: 856.Google Scholar
  9. 9.
    Santolaria, J., J. Conte and M. Ginés (2013). “Laser tracker-based kinematic parameter calibration of industrial robots by improved CPA method and active retroreflector.” The International Journal of Advanced Manufacturing Technology: 1–20.Google Scholar
  10. 10.
    Klimchik, A., A. Pashkevich, D. Chablat and G. Hovland (2013). “Compliance error compensation technique for parallel robots composed of non-perfect serial chains.” Robotics and Computer-Integrated Manufacturing 29(2): 385–393.Google Scholar
  11. 11.
    Chen, Y., J. Gao, H. Deng, D. Zheng, X. Chen and R. Kelly (2013). “Spatial statistical analysis and compensation of machining errors for complex surfaces.” Precision Engineering 37(1): 203–212.Google Scholar
  12. 12.
    Klimchik, A., D. Bondarenko, A. Pashkevich, S. Briot and B. Furet (2014). Compliance Error Compensation in Robotic-Based Milling. Informatics in Control, Automation and Robotics. J.-L. Ferrier, A. Bernard, O. Gusikhin and K. Madani, Springer International Publishing. 283: 197–216.Google Scholar
  13. 13.
    Zhuang, H., Z. S. Roth and F. Hamano (1992). “A complete and parametrically continuous kinematic model for robot manipulators.” Robotics and Automation, IEEE Transactions on 8(4): 451–463.Google Scholar
  14. 14.
    Zhuang, H., F. Adviser-Hamano and Z. S. Adviser-Roth (1989). “Kinematic modeling, identification and compensation of robot manipulators.”.Google Scholar
  15. 15.
    Yang, X., L. Wu, J. Li and K. Chen (2014). “A minimal kinematic model for serial robot calibration using POE formula.” Robotics and Computer-Integrated Manufacturing 30(3): 326–334.Google Scholar
  16. 16.
    Khalil, W., M. Gautier and C. Enguehard (1991). “Identifiable parameters and optimum configurations for robots calibration.” Robotica 9(01): 63–70.Google Scholar
  17. 17.
    Pashkevich, A. (2001). Computer-aided generation of complete irreducible models for robotic manipulators. The 3rd Int. Conference of Modellimg and Simulation. University of Technology of Troyes, France.Google Scholar
  18. 18.
    Klimchik, A., S. Caro and A. Pashkevich (2013). “Practical identifiability of the manipulator link stiffness parameters.” ASME 2013 International Mechanical Engineering Congress & Exposition: 1–10.Google Scholar
  19. 19.
    Khalil, W. and E. Dombre (2004). Modeling, identification and control of robots, Butterworth-Heinemann.Google Scholar
  20. 20.
    Wu, Y. (2014). Optimal Pose Selection for the Identification of Geometric and Elastostatic Parameters of Machining Robots, Phd Thesis, Ecole des Mines de Nantes, France.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alexandr Klimchik
    • 1
  • Stephane Caro
    • 2
  • Benoit Furet
    • 3
  • Anatol Pashkevich
    • 1
  1. 1.Ecole des Mines de NantesIrccynFrance
  2. 2.Centre National de la Recherche ScientifiqueIrccynFrance
  3. 3.University of NantesIrccynFrance

Personalised recommendations