A first approach to tactile texture recognition

Abstract

Tactile texture recognition seems to be a very important research area with various interesting applications which include medical, geological and autonomous mobile robotics; despite the great amount of tasks that biological beings solve using the sense of touch, not much work is done in this area (except for pressure-related sensing), and there are only a few papers in literature that uses dynamic tactile sensing strategies, i.e. the kind of exploration that we and the biological beings use to recognize a texture. In this paper we present a system for tactile texture recognition, using sound-understanding techniques. We develop a sensing "pen" with an electret piezoelectric microphone, covered by a rugged material. The pen is rubbed over the material that we want to identify, the sound produced is segmented, the FFT is obtained and the result is introduced to a learning vector quantization technique (LVQ). We explore 18 common materials which includes surfaces from glass to a real human beard. We achieve more than 93% of recognition over the 18 textures, and when the system makes an error it gives a similar texture as result.