Using Machine Learning for Material Detection with Capacitive Proximity Sensors

Abstract

The ability of detecting materials plays an important role in robotic applications. The robot can incorporate the information from contactless material detection and adapt its behavior in how it grasps an object or how it walks on specific surfaces. In this, paper we apply machine learning on impedance spectra from capacitive proximity sensors for material detection. The unique spectra of certain materials only differ slightly and are subject to noise and scaling effects during each measurement. A best-fit classification approach to pre-recorded data is therefore inaccurate. We perform classification on ten different materials and evaluate different classification algorithms ranging from simple k-NN approaches to artificial neural networks, which are able to extract the material specific information from the impedance spectra.