Recognizing tactile surface roughness with a biomimetic fingertip: A soft neuromorphic approach

Surface roughness is an important object property and can significantly affect the friction characteristics, wear resistance, and fatigue life of components. Although some work has been done on demonstrating the capability of specific tactile sensors for surface roughness discrimination, the soft neuromorphic approach by taking inspirations from neuroscience for tactile surface roughness discrimination is exceptionally rare. This paper aims to fill this gap, and presents a soft neuromorphic method for tactile surface roughness discrimination with a biomimetic fingertip. The analog tactile signals generated from polyvinylidene difluoride (PVDF) films are fed as input to the Izhikevich neurons in order to obtain spike trains. Two distinct decoding schemes based on k-nearest neighbors (kNN) in both spike feature space and spike train space are used for surface roughness discrimination. We thoroughly examined the different spike train distance based kNN (STD-kNN) algorithms for decoding spike trains. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (77.6 ± 13.7) % can be achieved with kNN (k = 11) classifier and the Victor−Purpura distance (q=0.024ms−1).