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A persistent method for parameter identification of a seven-axes manipulator

Published online by Cambridge University Press:  13 June 2014

Matthias Neubauer ,
Hubert Gattringer  and
Hartmut Bremer
Matthias Neubauer*
Affiliation:
Institute for Robotics, Johannes Kepler University, Linz 4040, Austria
Hubert Gattringer
Affiliation:
Institute for Robotics, Johannes Kepler University, Linz 4040, Austria
Hartmut Bremer
Affiliation:
Institute for Robotics, Johannes Kepler University, Linz 4040, Austria
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents a persistent method for the identification problem of open-chained robotic systems. Based on the Projection Equation, a new, direct method to collect the dynamic and friction parameters in linear form is worked out. However, in this form, linear dependencies in the parameters occur and they are canceled out with the help of the QR algorithm. The obtained linear independent parameters are the base parameters of the system. To ensure a good excitation, the identification is improved by using optimized trajectories defined by Fourier-series, taking also physical constraints into account. The evaluation of the dynamic robot parameters is realized with a least squares error optimization. Furthermore, the result strongly depends on a special choice of weighting matrices for the error. Experimental results for a seven-axes robotic system (standard six-axes industrial manipulator mounted on a linear axis) are presented in detail. Additionally, the influence of temperature effects to base parameter changes is discussed.

Keywords

Industrial robotsDynamic modelingParameter identification
Type
Articles
Information
Robotica , Volume 33 , Special Issue 5: Robotics in the Alpe-Adria-Danube Region (RAAD 2013) , June 2015 , pp. 1099 - 1112
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Albu-Schäffer, A., Regelung von Robotern mit elastischen Gelenken am Beispiel der DLR-Leichtbauarme Ph.D. Thesis (München, Germany: Technische Universität München, 2002).Google Scholar
2. Antonelli, G., Caccavale, F. and Chiacchio, P., “A systematic procedure for the identification of dynamic parameters of robot manipulators,” Robotica 17, 427–435 (1999).Google Scholar
3. Bejczy, A., “Robot arm dynamics and control,” Computer 15, 62–80 (1982).Google Scholar
4. Benimeli, F., Mata, V. and Valero, F., “A comparison between direct and indirect dynamic parameter identification methods in industrial robots,” Robotica 24, 579–590 (2006).CrossRefGoogle Scholar
5. Bremer, H., Dynamik und Regelung mechanischer Systeme (Teubner Studienbücher, Stuttgart, 1988).Google Scholar
6. Calafiore, G., Indri, M. and Bona, B., “Robot dynamic calibration: Optimal excitation trajectories and experimental parameter estimation,” J. Robot. Syst. 18, 55–68 (2001).3.0.CO;2-O>CrossRefGoogle Scholar
7. Craig, J., Introduction to Robotics: Mechanics and Control (Pearson Prentice Hall, New Jersey, 2004).Google Scholar
8. Gattringer, H., Riepl, R. and Neubauer, M., “Optimizing industrial robots for accurate high-speed applications,” J. Ind. Eng. 2013, 12 (2013).Google Scholar
9. Gautier, M., “Numerical calculation of base inertial parameters of robots,” J. Robot. Syst. 8, 485–506 (1991).Google Scholar
10. Gautier, M. and Khalil, W., “Exciting Trajectories for the Identification of Base Inertial Parameters of Robots,” Proceedings of the 30th IEEE Conference on Decision and Control, vol. 1, Brighton, England (Dec. 11–13, 1991) pp. 494–499.Google Scholar
11. ISO NORM 9283, Manipulating Industrial Robots–-Performance and Criteria. Norm, EN ISO 9283 (1998).Google Scholar
12. Khalil, W. and Dombre, E., Modeling, Identification and Control of Robots (Kogan Page Science, London, 2004).Google Scholar
13. Khalil, W. and Kleinfinger, J., “Minimum operations and minimum parameters of the dynamic models of tree structure robots,” IEEE J. Robot. Autom. RA–3 (6), 517–526 (1987).CrossRefGoogle Scholar
14. Khosla, P., “Categorization of parameters in the dynamic robot model,” IEEE Trans. Robot. Autom. 5, 261–268 (1989).Google Scholar
15. Kozlowski, K., Modelling and Identification in Robotics (Springer Verlag, London, 1998).Google Scholar
16. Kozlowski, K. R. and Dutkiewicz, P., “Experimental identification of dynamic parameters for a class of geared robots,” Robotica 14, 561–574 (1996).Google Scholar
17. Pfeiffer, F. and Hölzl, J., “Parameter Identification for Industrial Robots,” Proceedings of the 1995 IEEE International Conference on Robotics and Automation, vol. 2, Nagoya, Aichi, Japan (May 21–27, 1991) pp. 1468–1476.Google Scholar
18. Sciavicco, L. and Siciliano, B., Modelling and Control of Robot Manipulators (Springer Verlag, London, 2004).Google Scholar
19. Siciliano, B. and Khatib, O., eds., Handbook of Robotics (Springer Verlag, Berlin, Heidelberg, 2008).CrossRefGoogle Scholar
20. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics–-Modelling, Planning and Control (Springer Verlag, London, 2009).Google Scholar
21. Swevers, J., Ganseman, C., Tükel, D., Schutter, J. D. and Brussel, H. V., “Optimal robot excitation and identification,” IEEE Trans. Robot. Autom. 13, 730–740 (1997).Google Scholar