Texture Detection by Hardware-Friendly Low-Level Preprocessing

The aim of having artificial hands for robots or prosthetics that achieve fluent manipulation requires the efficient management of data from many sensors, and particularly from tactile sensors. Texture recognition is commonly carried out with tactile sensors, and active touch allows the detection of features beyond the limits imposed by the size and spatial resolution of the sensor. This paper assesses the feasibility of two hardware-friendly preprocessing algorithms to detect different textures. The proposed approach is focused on the implementation of simple hardware that can be replicated in the local electronics of a smart sensor to process the data from every force sensing unit or tactel in the tactile array. Experimental results from periodic textures are provided to show the feasibility of the approach. For instance, the processing of the output of a single tactel provides 13.27 mm for a 13 mm wavelength sample, and 1.66 mm for a wavelength of 1.67 mm, when the spatial resolution of the sensor is 3.70 mm. This solution is not enough for non-periodic textures, but provide useful information about the coarseness of the surface.