Hostname: page-component-7b7567c8c-tnzcf Total loading time: 0 Render date: 2026-11-12T19:35:51.845Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of adaptive neural network to localization of objects using pressure array transducer

Published online by Cambridge University Press:  09 March 2009

Anderson Leung and
Shahram Payandeh
Anderson Leung
Affiliation:
Experimental Robotics Laboratory (ERL), School of Engineering Science Simon Fraser University Burnaby, British Columbia, (Canada) V5A 1S6 shahram@cs.sfu.ca
Shahram Payandeh
Affiliation:
Experimental Robotics Laboratory (ERL), School of Engineering Science Simon Fraser University Burnaby, British Columbia, (Canada) V5A 1S6 shahram@cs.sfu.ca
Get accessRights & Permissions [Opens in a new window]

Summary

Pattern recognition and object localization, using various sensors such as vision and tactile sensors, are two important areas of research in the application of robotic systems. This paper demonstrates the feasibility of using some relatively inexpensive array of pressure sensors and a neural network approach to achieve object localization and pattern recognition. The sensors that are used are force sensing resistors (FSRs), more specifically, a 16 x 16 array of FSRs. Because of the nonlinearity associated with a FSR, three possible approaches for gathering output from the sensor array are used. The neural network that is used consists of two 2-layer counterpropagation networks (CPNs). One of the CPNs is trained to recognize contact signatures of different objects placed on a fixed reference position on the sensor array.

Keywords

Neural networkPressure array transducerPattern recognitionObject localizationSensors

Information

Type
Article
Information
Robotica , Volume 14 , Issue 4 , July 1996, pp. 407 - 414
Copyright
Copyright © Cambridge University Press 1996

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.)

Article purchase

Temporarily unavailable

References

1.Sugisaka, M. and Teshnehlab, M.. “Fast pattern recognition by using moment invariants computation via artificial neural networks” Control-Theorv and Advanced Technology 9, No. 4. 877–886(1993).Google Scholar
2.Russell, R.A.. “Robot Tactile Sensing” (Prentice Hall. Hemel Hempstead. UK, 1990).Google Scholar
3.Math, W.S. and Yeung, S.K., “Tactile sensor for geometric profile perception” IEEE IECON 2, (1991) pp. 893–897.Google Scholar
4.Sperduti, A. and Starita, A., “Hand control by a neural network using tactile and positional information” Proceedings of ICAR(1991) pp. 1747–1751.Google Scholar
5.Desai, A., Payandeh, S. and Vaisey, J., “On the localization of objects using an FSR pressure pad transducer” Proceedings of IEEE SMC Conference(1994) pp. 953–957.Google Scholar
6.Spencer, R.R. and Worth, A.J.. “A Neural Network for Tactile Sensing: the Hertzian Contact Problem” Proceedings of IEEE Conference on Neural Networks(1989) pp. 267–274.Google Scholar
7.Miiller, B. and Reinhardt, J., Neural Network-An Introduction. (Springer-Verlag. Berlin. 1991).Google Scholar
8.Freeman, J.A. and Skapura, D.M., Neural Networks Algorithms, Applications, and Programming Techniques, (Addison-Wesley. Wokingham. UK. 1992).Google Scholar
9.Karplus, W. and Skrzypek, J.. Neural Networks in Vision and Pattern Recognition, 3 (World Scientific, London. 1992).Google Scholar