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Tactile discrimination using active whisker sensors

Lepora, Nathan F.; Sullivan, J. Charles; Fox, Charles W.; Pearson, Martin; Mitchinson, Ben; Evans, Mat; Melhuish, Chris; Prescott, Tony J.


Nathan F. Lepora

J. Charles Sullivan

Charles W. Fox

Ben Mitchinson

Mat Evans

Chris Melhuish
AVC - Advanced Technologies & Professor of Robotics & Autonomous Systems

Tony J. Prescott


We describe a novel, biomimetic tactile sensing system modeled on the facial whiskers (vibrissae) of animals such as rats and mice. The "BIOTACT Sensor" consists of a conical array of modular, actuated hair-like elements, each instrumented at the base to accurately detect deflections of the shaft by whisker-surface contacts. A notable characteristic of this array is that, like the biological sensory system it mimics, the whiskers are moved back-and-forth ("whisked") so as to make repeated, brief contacts with surfaces of interest. Furthermore, these movements are feedback-modulated in a manner intended to emulate some of the "active sensing" control strategies observed in whiskered animals. We show that accurate classification of surface texture using data obtained from whisking against three different surfaces is achievable using classifiers based on either naive Bayes or template methods. Notably, the performance of both these approaches to classify textures after training on as few as one or two surface contacts was improved when the whisking motion was controlled using a sensory feedback mechanism. We conclude that active vibrissal sensing could likewise be a useful sensory capacity for autonomous robots. © 2011 IEEE.


Fox, C. W., Lepora, N. F., Pearson, M. J., Sullivan, J. C., Sullian, J., Mitchinson, B., …Prescott, T. J. (2012). Tactile discrimination using active whisker sensors. IEEE Sensors Journal, 12(2), 350-362.

Journal Article Type Article
Publication Date Jan 1, 2012
Journal IEEE Sensors Journal
Print ISSN 1530-437X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 12
Issue 2
Pages 350-362
Keywords Bayesian methods, biomimetics, classification algorithms,
robotics, sensor systems and applications, tactile sensors
Public URL
Publisher URL