A Novel Method for Soft Contact Sensing Based on Electrical Impedance Sensitivity Images

Abstract

This paper presents a distributed soft contact sensing method based on a sensitivity mapping function, which relates the change in measured voltages to that in the elastomer conductivity due to contact force acting on its surface. The sensitivity-image-based sensing system uses a small number of boundary electrodes with a multiplexer to create different electric-field patterns to generate a series of sensitivity images for machine learning, significantly reducing the number of training data typically obtained with single-point indentation measurements. The mapping function, which does not rely on the knowledge of the electric and conductivity fields during online sensing, can be trained with only a small amount of measured data in the order of the square of electrode number. The proposed method has been experimentally evaluated on two 16-electrode prototypes trained with 129 and 80 data for two typical applications, which are tactile perception on a flat surface and contact force measurement in a model knee joint, respectively. The former verifies the measurement accuracy of the contact position and force magnitude while the latter demonstrates the application of this method for measuring the internal joint forces between two curved surfaces.