Abstract
In this paper we present an approach for material recognition using capacitive tactile and proximity sensors. By variating the spatial resolution and the exciter frequency during the measurement in mutual capacitive mode, information about the dielectrical properties of different objects was captured and provided as data frames. For material recognition an artificial neural network was set up and fed with various data sets of different electrode combinations and exciter frequencies. The influence of the electrode combinations and shapes on the recognition accuracy was investigated. It is shown that seven objects of conductive and non-conductive dielectric materials have been ranged with an overall accuracy of about 71%-94%.
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