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A new approach to the dynamic parameter identification of robotic manipulators

Published online by Cambridge University Press:  24 June 2009

Zhongkai Qin ,
Luc Baron  and
Lionel Birglen
Zhongkai Qin*
Affiliation:
Department of Mechanical Engineering, École Polytechnique, Montréal, Québec, CanadaH3C 3A7
Luc Baron
Affiliation:
Department of Mechanical Engineering, École Polytechnique, Montréal, Québec, CanadaH3C 3A7
Lionel Birglen
Affiliation:
Department of Mechanical Engineering, École Polytechnique, Montréal, Québec, CanadaH3C 3A7
*
*Corresponding author. E-mail: zhongkai.qin@polymtl.ca
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Summary

This paper presents a novel systematic approach to identify the dynamic parameters of robotic manipulators. A sequential identification procedure is first proposed to deal with the difficulties usually encountered with standard approaches. An all-accelerometer inertial measurement unit (IMU) is also suggested to estimate the joint velocities and accelerations, which are traditionally obtained by differentiating the joint positions. The IMU kinematics and the computation method for estimation joint motion from IMUs are provided. The proposed method yields promising results in improving the identification precision compared to conventional methods. Finally, practical experiments are conducted to validate the theoretical results.

Keywords

Robot dynamicsDynamic identificationRobotic manipulatorSerial robotInertial measurement

Information

Type
Article
Information
Robotica , Volume 28 , Issue 4 , July 2010 , pp. 539 - 547
Copyright
Copyright © Cambridge University Press 2009

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References

1.Armstrong, B., “On finding exciting trajectories for identification experiments involving systems with nonlinear dynamics,” Int. J. Robot. Res. 8 (6), 28–48 (1989).CrossRefGoogle Scholar
2.Armstrong, B., Khatib, O. and Burdick, J., “Explicit Dynamic Model and Inertial Parameters of the Puma 560 Arm,” Proceedings of the IEEE International Conference Robotics and Automation, San Francisco, CA, USA (1986) pp. 510–518.Google Scholar
3.Chan, S. P., Learning Friction Compensation in Robot Manipulators, vol. 3. (Maui, HI, USA, 1993) pp. 2282–2287.Google Scholar
4.Craig, J. J., Introduction to Robotics: Mechanics and Control, 2nd ed. (Addison Wesley, Reading, Massachusetts, 1989).Google Scholar
5.Gautier, M. and Khalil, W., “On the Identification of the Inertial Parameters of Robots,” Proceedings of the IEEE Conference on Decision and Control Including The Symposium on Adaptive Processes, Austin, TX, USA (1988) pp. 2264–2269.Google Scholar
6.Gautier, M. and Khalil, W., “Direct calculation of minimum set of inertial parameters of serial robots,” IEEE Trans. Robot. Autom. 6 (3), 368–373 (1990).CrossRefGoogle Scholar
7.Gautier, M. and Khalil, W., “Exciting Trajectories for the Identification of Base Inertial Parameters of Robots,” Proceedings of the IEEE Conference on Decision and Control, Brighton, England, UK (1992) pp. 494–499.Google Scholar
8.Gautier, M. and Presse, C., “Sequential Identification of Base Parameters of Robots,” 91 ICAR. Fifth International Conference on Advanced Robotics. Robots in Unstructured Environments (Cat. No.91TH0376-4), Pisa, Italy (1991) pp. 1105–1110.Google Scholar
9.Khosla, P. K., “Estimation of robot dynamics parameters: theory and application,” Int. J. Robot. Autom. 3 (1), 35–41 (1988).Google Scholar
10.Olsen, M. M., Swevers, J. and Verdonck, W., “Maximum likelihood identification of a dynamic robot model: Implementation issues,” Int. J. Robot. Res. 21 (2), 89–96 (2002).CrossRefGoogle Scholar
11.Olsson, H., Astrom, K. J., De Wit, C. C., Gafvert, M. and Lischinsky, P., “Friction models and friction compensation,” Eur. J. Control 4 (3), 176–195 (1998).CrossRefGoogle Scholar
12.Papadopoulos, E. G. and Chasparis, G. C., “Analysis and model-based control of servomechanisms with friction,” J. Dyn. Syst. Meas. Control Trans. ASME 126 (4), 911–915 (2004).CrossRefGoogle Scholar
13.Presse, C. and Gautier, M., “New Criteria of Exciting Trajectories for Robot Identification,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, Atlanta, GA, USA (1993) pp. 907–912.Google Scholar
14.Qin, Z., Baron, L. and Birglen, L., “Robust design of all-accelerometer based inertial measurement unit,” J. Dyn. Syst. Meas. Control 131 (3), (2009).CrossRefGoogle Scholar
15.Sciavicco, L. and Siciliano, B., Modelling and Control of Robot Manipulators. (Springer, London, 2000).CrossRefGoogle Scholar
16.Seeger, G. and Leonhard, W., “Estimation of Rigid Body Models for a Six-axis Manipulator with Geared Electric Drives,” Proceedings. 1989 IEEE International Conference on Robotics and Automation (Cat. No.89CH2750-8), Scottsdale, AZ, USA (1989) pp. 1690–1695.CrossRefGoogle Scholar
17.Swevers, J., Ganseman, C., De Schutter, J. and Van Brussel, H., “Experimental robot identification using optimised periodic trajectories,” Mech. Syst. Signal Process. 10 (5), 561–577 (1996).CrossRefGoogle Scholar
18.Swevers, J., Verdonck, W., Naumer, B., Pieters, S. and Biber, E., “An experimental robot load identification method for industrial application,” Int. J. Robot. Res. 21 (8), 701–712 (2002).CrossRefGoogle Scholar
19.Zakharov, A. and Halasz, S., “Genetic Algorithms based Identification Method for a Robot Arm,” IEEE International Symposium on Industrial Electronics, Bled, Slovenia, 3, 1014–1019 (1999).Google Scholar