single-jc.php

JACIII Vol.14 No.1 pp. 39-45
doi: 10.20965/jaciii.2010.p0039
(2010)

Paper:

Experimental Identification of Manipulator Dynamics Through the Minimization of its Natural Oscillations

Rodrigo S. Jamisola, Jr. and Elmer P. Dadios

De La Salle University, 2401 Taft Ave, 1004 Manila, Philippines

Received:
April 22, 2009
Accepted:
August 28, 2009
Published:
January 20, 2010
Keywords:
dynamics identification, experimental method, optimization, natural oscillation
Abstract

This work presents amethod of identifying the dynamics parameters of rigid-body manipulators through the minimization of its natural oscillations. It is assumed that each link has an actuated joint that is different from its center of mass, such that its driving torque is influenced by gravitational force. In this earlier results of our study, it is assumed that the inertias can be expressed in terms of the mass and center of mass. This work utilizes the actual force of gravity for the manipulator link to achieve natural oscillation. The oscillatory motion allows the system to be converted into an optimization problem through the minimization of the frequency of oscillation. The correct dynamics parameters are found when the minimum frequency of oscillation is achieved. The proposed method is analyzed and a theorem is presented that supports the claims presented in this work together with implementation results.

Cite this article as:
Rodrigo S. Jamisola, Jr., and Elmer P. Dadios, “Experimental Identification of Manipulator Dynamics Through the Minimization of its Natural Oscillations,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.1, pp. 39-45, 2010.
Data files:
References
  1. [1] C. H. An, C. G. Akteson, and J. M. Hollerbach, “Estimation of Inertial Parameters of Rigid Body Links of Manipulators,” Proc. of the 24th Conf. on Decision and Control, pp. 990-1002, 1985.
  2. [2] J. J. Craig, “Adaptive Control of Mechanical Manipulators,” Ph.D. thesis, Stanford University, 1986.
  3. [3] B. Armstrong, “On finding ‘exciting’ trajectories for identification experiments involving systems with non-linear dynamics,” Proc. IEEE Int. Conf. on Robotics and Automation, Vol.4, pp. 1131-1139, 1987.
  4. [4] M. Gautier, A. Janot, and P. Vandanjon, “DIDIM: A New Method for the Dyanmic Identification of Robots from only Torque Data,” Proc. of the 2008 IEEE Int. Conf. on Robotics and Automation, pp. 2122-2127, 2008.
  5. [5] K. Ayusawa, G. Venture, and Y. Nakamura, “Identification of Humanoid Robots Dynamics using Floationg-base Motion Dymanics,” Proc. of the 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2854-2859, 2008.
  6. [6] B. Bukkens, D. Kostić, B. d. Jager, and M. Steinbuch, “Learning-Base Idetification and Iterative Learning Control of Direct-Drive Robots,” IEEE Trans. on Control Systems Technology, Vol.13, No.4, pp. 537-549, 2005.
  7. [7] Z.-H. Jiang, T. Ishida, and M. Sunawada, “Neural Network Aided Dynamic Parameter Identification of Robot Manipulators,” Proc. of the 2006 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 3298-3303, 2006.
  8. [8] N. Ramdani and P. Poignet, “Robust Dynamic Experimental Identification of Robots With Set Membership Uncertainty,” IEEE/ASME Trans. on Mechatronics, Vol.10, No.2, pp. 253-256, 2005.
  9. [9] D. J. Austin, “Simultaneous Identification and Control of a Hybrid Dynamic Model for a Mobile Robot”, Proc. of the 39th Conf. on Decision and Control, pp. 3138-3143, 2000.
  10. [10] N. A. Bompos, P. K. Artemiadis, A. S. Oikonomopoulos, and K. J. Kyriakopoulos, “Modeling, Full Identification and Control of the Mitsubishi PA-10 Robot Arm,” Proc. of the 2007 IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics (AIM2007), pp. 1-6, 2007.
  11. [11] D. Kostić, B. d. Jager, M. Steinbuch, and R. Hensen, “Modeling and Identification for High-Performance Robot Control: An RRRRobotic Arm Case Study,” IEEE Trans. on Control Systems Technology, Vol.12, No.6, pp. 904-919, 2004.
  12. [12] K. Radkhah, D. Kulic, and E. Croft, “Dynamic Parameter Identification for the CRS A460 Robot,” Proc. of the 2007 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3842-3847, 2007.
  13. [13] J. Swevers, W. Verdonck, and J. D. Schutter, “Dynamic Model Identification for Industrial Robots,” IEEE Control Systems Magazine, Vol.27, pp. 58-71, 2007.
  14. [14] K. Noda, M. Ito, Y. Hoshino, and J. Tani, “Dynamic Generation and Switching of Object Handling Behaviors by a Humanoid Robot Using a Recurrent Neural Network Model,” 9th Int. Conf. on Simulation and Adaptive Behavior, Vol.4095, pp. 185-196, 2006.
  15. [15] V. Ivancevic and M. Snoswell, “Fuzzy-Stochastic Functor Machine for General Humanoid-Robot Dynamics,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.31, No.3, pp. 319-330, 2001.
  16. [16] R. S. Jamisola, Jr.,M. Ang, Jr., T. M. Lim, O. Khatib, and S. Y. Lim, “Dynamics Identification and Control of an Industrial Robot,” Proc. 9th Int. Conf. on Advanced Robotics, pp. 323-328, 25-27 1999.
  17. [17] R. S. Jamisola, Jr., D. N. Oetomo, J. M. H. Ang, O. Khatib, T. M. Lim, and S. Y. Lim, “Compliant Motion using a Mobile Manipulator: An Operational Space Formulation Approach to Aircraft Canopy Polishing,” RSJ Advanced Robotics, Vol.19, No.5, pp. 613-634, 2005.
  18. [18] O. Khatib, “Inertial Properties in Robotic Manipulation: An Object-Level Framework,” Int. J. of Robotics Research, Vol.14, No.1, pp. 19-36, 1995.
  19. [19] O. Khatib, “Real-time Obstacle Avoidance for Manipulators and Mobile Robots,” Int. J. of Robotics Research, Vol.5, No.1, pp. 90-98, 1986.
  20. [20] L. E. Kavraki, P. Švestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces,” IEEE Trans. on Robotics and Automation, Vol.12, No.4, pp. 566-580, 1996.
  21. [21] R. S. Jamisola, Jr., A. A. Maciejewski, and R. G. Roberts, “Failure-Tolerant Path Planning for Kinematically Redundant Manipulators Anticipating Locked-Joint Failures,” IEEE Trans. on Robotics, Vol.22, No.4, pp. 603-612, 2006.
  22. [22] A. A. Maciejewski and C. A. Klein, “Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments,” Int. J. of Robotics Research, Vol.4, No.3, pp. 109-117, 1985.
  23. [23] Z. Tang, M. J. Er, and G. S. Ng, “Humanoid Robotics Modelling by Dynamic Fuzzy Neural Network,” Proc. of Int. Joint Conf. on Neural Networks, pp. 2653-2657, 2007.
  24. [24] T. Yamamoto and Y. Kuniyoshi, “Stability and controllability in a rising motion: a global dynamics approach,” Proc. of the 2002 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2467-2472, 2002.
  25. [25] J. Park, Y. Youm, and W.-K. Chung, “Control of Ground Interaction at Zero-Moment Point for Dynamic Control of Humanoid Robots,” Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation, pp. 1724-1729, 2005.
  26. [26] K. S. Fu, R. C. Gonzales, and C. S. G. Lee, “Robotics: Control, Sensing, Vision, and Intelligence,” McGraw-Hill, Inc., U.S.A., 1987.
  27. [27] K. Ogata, “Modern Control Engineering,” Printice-Hall, Inc., 4th edition, 2002.

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

Last updated on Feb. 25, 2021