Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-27T04:39:03.791Z Has data issue: false hasContentIssue false
  • English
  • Français

The identification of joint parameters for modular robots using fuzzy theory and a genetic algorithm

Published online by Cambridge University Press:  06 September 2002

Yangmin Li ,
Xiaoping Liu ,
Zhaoyang Peng  and
Yugang Liu
Yangmin Li
Affiliation:
Faculty of Science and Technology, University of Macau, Macao SAR (P.R. China).
Xiaoping Liu
Affiliation:
Automation School, Beijing University of Posts and Telecommunications, Beijing (P.R. China).
Zhaoyang Peng
Affiliation:
Automation School, Beijing University of Posts and Telecommunications, Beijing (P.R. China).
Yugang Liu
Affiliation:
Automation School, Beijing University of Posts and Telecommunications, Beijing (P.R. China).
Get access Rights & Permissions [Opens in a new window]

Summary

This paper discusses a technique for identifying the joint parameters of a modular robot in order to study the dynamic characteristics of the whole structure and to realise dynamic control. A method for identifying the joint parameters of the structure applying fuzzy logic combined with a genetic algorithm has been studied using a 9-DOF modular redundant robot. A Genetic Algorithm was used in the fuzzy optimisation, which helped to avoid converging to locally optimal solutions and made the results identified much more reasonable. The joint parameters of a 9-DOF modular redundant robot have been identified.

Keywords

Parameter identificationModular robotsModal analysisVibration control
Type
Research Article
Information
Robotica , Volume 20 , Issue 5 , September 2002 , pp. 509 - 517
Copyright
Copyright © Cambridge University Press 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Schmitz, D., Khosla, P. and Kanade, T., “The CMU Reconfigurable Modular Manipulator System,” Technical Report CMU-RI-TR-88-07. (Robotics Institute, Carnegie Mellon University, May, 1988).Google Scholar
2. Chen, I. and Yang, G., “Inverse Kinematics for Modular Reconfigurable Robots,” Proc. of the IEEE International Conference on Robotics and Automation, Leuven, Belgium (May 1998), 1647–1652.Google Scholar
3. Fei, Y., Zhao, X. and Song, L., “A method for modular robots generating dynamics automatically,” Robotica 19, Part 1, 59–66 (2001).Google Scholar
4. Liu, X. and Yang, B., “Structure vibration control by tuned distributed vibration damper,” ASME Design Engineering Technique Conference. Las Vegas, Nevada, USA (Sept 12–15, 1999), 1–9.Google Scholar
5. Behi, F. and Tesar, D., “Parametic Identification for Industrial Manipulation Using Experimental Modal Analysis,” IEEE Transactions on Robotics and Automation 7(5), 642–652 (1991).CrossRefGoogle Scholar
6. Adhikari, S. and Woodhouse, J., “Identification of Damping: Part 1, Viscous Damping,” Journal of Sound and Vibration 243(1), 43–61 (2001).Google Scholar
7. Adhikari, S. and Woodhouse, J., “Identification of Damping: Part 2, Non-Viscous Damping,” Journal of Sound and Vibration 243(1), 63–88 (2001).Google Scholar
8. Olsen, M. M. and Peterson, H. G., “A new Method for Estimating Parameters of a Dynamic Robot Model,” IEEE Robotics and Automation 17(1), 95–100 (2001).Google Scholar
9. Pham, M. T., Gautier, M. and Poignet, P., “Identification of joint stiffness with bandpass filtering,” Proc. of the IEEE International Conference on Robotics and Automation. Seoul, Korea (May 21–26, 2001), 2867–2872.Google Scholar
10. Li, Y., Xie, C. and Liu, Y., “Parameter Identification on Puma 760 Robot Dynamics,” The Second Asia Conference on Robotics and Its Application. Beijing, P. R. China (Oct, 1994), 155–158.Google Scholar