Surface Recognition With a Bioinspired Tactile Fingertip

Abstract

With the endowed ability to perceive various objects on touch, human is capable of manipulating them dexterously and interacting with surrounding environments, which inspires a huge number of tactile sensors to spring up. In this article, we designed an improved tactile fingertip for robotic manipulation. Bioinspired by the fast-adapting (FA) and slow-adapting (SA) types of mechanoreceptors within human skin, the fingertip contains two polyvinylidene fluoride (PVDF) films and two strain gauges as sensing elements, enabling the perception of both dynamic and static forces. Meanwhile, the structure of human fingertip is mimicked in the design that sensing elements are covered by soft “skin,” while a stiff bar plays the role of phalanx. To realize one of the typical haptic abilities, surface recognition, an efficient signal processing framework is proposed to focus on the extraction and selection of the most discriminative features rather than paying excess attention to various feature definitions. The selection algorithm, neighborhood component analysis, is combined with a linear discriminant classification model. Through a robotic experiment, the effectiveness of the tactile fingertip as well as the recognition method is validated, aimed at recognizing ten object surfaces in our daily life. The final recognition accuracy reaches as high as 99.52%. Furthermore, several typical algorithms for feature selection and object classification are compared, while the influence of dataset parameters on the prediction performance is investigated.

Topics & Concepts