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

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