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Robot Geometric Parameter Identification with Extended Kalman Filtering Algorithm

  • Hoai-Nhan Nguyen
  • Jian Zhou
  • Hee-Jun Kang
  • Young-Shick Ro
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

This paper proposes a calibration method for enhancing position accuracy of robotic manipulators. In order to increase the robot accuracy, the method first develops a robot kinematic model and then identifies the robot geometric parameters by using an extended Kalman filtering (EFK) algorithm. The Kalman filter has advantages in identifying geometric parameters from the noisy measurements. Therefore, the obtained kinematic parameters are more precise. A simulation study of this calibration is performed for a PUMA 560 robot to prove the effectiveness of the method in increasing robot position accuracy.

Keywords

robot calibration extended Kalman filter parameter identification 

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References

  1. 1.
    Elatta, A.Y., et al.: An Overview of Robot Calibration. Infor. Tech. J. 3, 74–78 (2004)CrossRefGoogle Scholar
  2. 2.
    Mooring, B.W., et al.: Fundamental of Manipulator calibration. John Wiley & Son (1991)Google Scholar
  3. 3.
    To, M., Webb, P.: An improved kinematic model for calibration of serial robots having closed-chain mechanisms. Robotica, 1-9 (2011)Google Scholar
  4. 4.
    Gong, C., Yuan, J., Ni, J.: Non-geometric error identification and compensation for robotic system by inverse calibration. Int. J. of M. Tools and Manu. 40(14), 2119–2137 (2000)CrossRefGoogle Scholar
  5. 5.
    Judd, R.P., Knasinski, A.B.: A Technique to Calibrate Industrial Robots with Experimental Verification. IEEE Trans. on Robotics and Automation 6(1), 20–30 (1990)CrossRefGoogle Scholar
  6. 6.
    Aoyagi, S., et al.: Improvement of Robot Accuracy by Calibrating Kinematic Model Using a Laser Tracking System, Compensation of Non-Geometric Errors Using Neural Networks and Selection of Optimal Measuring Points Using Genetic Algorithm. In: IEEE/ Int. Conf. on Intelligent Robots and Systems, pp. 5660–5665 (2010)Google Scholar
  7. 7.
    Joon, H.J., Soo, H.K., Yoon, K.K.: Calibration of geometric and non-geometric errors of an industrial robot. Robotica 19(3), 311–321 (2001)Google Scholar
  8. 8.
    Zak, G., et al.: Application of the Weighted Least Squares Parameter Estimation Method to the Robot Calibration. J. of Mechanical Design/Trans. of ASME 116, 890–893 (1994)CrossRefGoogle Scholar
  9. 9.
    Park, I.W., et al.: Laser-Based Kinematic Calibration of Robot Manipulator Using Differential Kinematics. IEEE/ASME Trans. on Mechatronics 99, 1–9 (2011)Google Scholar
  10. 10.
    Hartenberg, R.S., Denavit, J.: A kinematic notation for lower pair mechanisms based on matrices. Trans. ASME/ J. of Applied Mechanics 77, 215–221 (1955)MathSciNetGoogle Scholar
  11. 11.
    Graig, J.J.: Introduction to Robotics: Mechanics and Control, 2nd edn. Add. Wiley (1989)Google Scholar
  12. 12.
    Hayati, S., Tso, K., Roston, G.: Robot Geometry Calibration. In: Proc. IEEE Int. Conf. on Robotics and Automation, vol. 2, pp. 947–951 (1988)Google Scholar
  13. 13.
    Veitschegger, W., Wu, C.-H.: Robot Accuracy Analysis Based on Kinematics. IEEE Journal of Robotics and Automation 2(3), 171–179 (1986)Google Scholar
  14. 14.
    Bennett, D.J., Hollerbach, J.M.: Autonomous Calibration of Single-Loop Closed Kinematic Chains Formed by Manipulators with Passive Endpoint Constraints. IEEE Transactions on Robotics and Automation 7(5), 597–606 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hoai-Nhan Nguyen
    • 1
  • Jian Zhou
    • 1
  • Hee-Jun Kang
    • 2
  • Young-Shick Ro
    • 2
  1. 1.Graduate School of Electrical EngineeringUniversity of UlsanUlsanKorea
  2. 2.School of Electrical EngineeringUniversity of UlsanUlsanKorea

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