VibroTact: Soft Piezo Vibration Fingertip Sensor for Recognition of Texture Roughness via Robotic Sliding Exploratory Procedures

Abstract

This letter presents a low-cost soft fingertip sensor for recognition of texture roughness. This sensor is designed with a soft rounded shape made of silicone rubber. An array of flexible piezo vibration elements, embedded in the sensor, measures vibrations while interacting with an object surface. The sensor is mounted on a robot arm for data collection with sliding exploratory procedures. The soft fingertip sensor is validated with roughness recognition of eight artificial textures using five different sliding directions (horizontal, vertical, left diagonal, right diagonal, and square). The sensor uses four machine learning methods [K-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), and convolutional neural networks) for recognition and comparison processes. The tactile sensor can recognize textures with accuracies from 90.60% to 100% for different sliding directions and computational methods, where the horizontal sliding with SVM and KNN methods achieved the highest performance. This tactile sensor has the potential to extract surface properties for autonomous object exploration, recognition, and manipulation tasks.