20.5 Conclusions
By using the dynamic on-line fuzzy interpolation technique presented in this chapter, the position errors in a modeless robot calibration can be greatly reduced. The effectiveness of using the proposed technique is confirmed by the simulation studies, in which three typical error models, sinusoid waveform, normal distributed and uniform distributed errors, are utilized. This fuzzy interpolation technique is suitable for the modeless robot position compensation. One drawback of the proposed method is its computational complexity in comparison to the linear interpolation method. With the advance of real-time operating systems, the difficulty due to this problem for on-line applications will be greatly reduced.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
B. W. Mooring, Z. S. Roth and M. R. Driels, Fundamentals of Manipulator Calibration, Wiley, 1991.
Hanqi Zhuang and Z. S. Roth, Camera-Aided Robot Calibration, CRC Press, Inc. 1996.
J. S. Shamma and D. E. Whitney, A method for inverse robot calibration. Journal of Dynamic Systems, Measurement, and Control, 109(1): 36–43, March 1987.
J. S. Shamma, A Method for Inverse Robot Calibration, Master’s thesis, Massachusetts Institute of Technology, January 1985.
J. S. Albus, “Data storage in the cerebella model articulation controller (CMAC),” Transactions of the ASME Journal of Dynamic Systems, Measurement, and Control, 97:228–233, September 1975.
J. S. Albus, “A new approach to manipulator control: The cerebella model articulation controller (CMAC),” Transactions of the ASME Journal of Dynamic Systems, Measurement, and Control, 97:220–227, September 1975.
H. Zhuang and Xiaomin Wu, “Membership function modification of fuzzy logic controllers with histogram equalization,” IEEE Trans. On System, Man, and Cybernetics — Part B: Cybernetics, Vol. 31, No. 1, February 2001, pp 125–131.
B. Carlson and C. Looney, “A new method of image interpolation,” Proceedings of the ISCA 16th International Conference on Computers and their Applications, Seattle Washington, March 28–30, 2001, pp 25–28.
F. Song, S. M. Smith, and C. G. Rizk, “A fuzzy logic controller design methodology for 4D system with optimal global performance using enhanced cell state space based best estimate directed search method,” IEEE Int. Conf. On Systems, Man and Cybernetics, Tokyo, Japan, Oct 12, 1999, pp 138–143.
Ying Bai and Dali Wang, “Improve the position measurement accuracy using a fuzzy error interpolation technique,” Proceedings Of The IEEE 2003 International Symposium On Computational Intelligence For Measurement Systems and Applications. July 29–31, 2003, Lugano, Switzerland, pp 227–232.
Ying Bai and Dali Wang, “On the comparison of interpolation techniques for robotic position compensation,” 2003 IEEE International Conference on Systems, Man and Cybernetics, October 5–8, 2003, Washington DC, pp 3384–3389.
Ned Gulley and J.-S. Roger. Jang, Fuzzy Logic Toolbox User Guide, The MathWorks, Inc. 1995.
Ying Bai, Applications Interface Programming Using Multiple Languages — A Windows Programmer’s Guide, Prentice Hall, March 2003.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag London Limited
About this chapter
Cite this chapter
Bai, Y., Wang, D. (2006). Fuzzy Logic for Robots Calibration — Using Fuzzy Interpolation Technique in Modeless Robot Calibration. In: Bai, Y., Zhuang, H., Wang, D. (eds) Advanced Fuzzy Logic Technologies in Industrial Applications. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-84628-469-4_20
Download citation
DOI: https://doi.org/10.1007/978-1-84628-469-4_20
Publisher Name: Springer, London
Print ISBN: 978-1-84628-468-7
Online ISBN: 978-1-84628-469-4
eBook Packages: EngineeringEngineering (R0)