Contact Localization and Force Estimation of Soft Tactile Sensors Using Artificial Intelligence

Abstract

Soft artificial skin sensors that can detect contact forces as well as their locations are attractive in various soft robotics applications. However, soft sensors made of polymer materials have inherent limitations of hysteresis and nonlinearity in response, which makes it highly difficult to implement traditional calibration techniques and yields poor estimation performance. In this paper, we propose intelligent algorithms based on machine learning and logics that can improve the performance of soft sensors. The proposed methods in this paper could be solutions to the aforementioned long-standing problems. They can also be used to simplify the system complexity by reducing the number of signal wires. Three machine learning techniques are discussed in this paper: an artificial neural network (ANN), the k-nearest neighbors (k-NN) algorithm, and a recurrent neural network (RNN). The Preisach model of hysteresis and simple logics were used to support these algorithms. We proved that classifying contact locations on a soft sensor is possible using simple algorithms in real time. Also, force estimation of a single contact was possible using an ANN with the Preisach method. Finally, we successfully estimated forces of multiple contact locations by predicting the outputs of mixed RNN results.