Artificial Skin Ridges Enhance Local Tactile Shape Discrimination

TL;DR

This paper demonstrates that artificial skin ridges, inspired by human fingerprints, significantly improve the accuracy of tactile surface curvature discrimination in robotic fingertips using machine learning methods.

Contribution

The study introduces a ridged fingertip design combined with machine learning algorithms to enhance tactile shape discrimination in artificial skins, achieving high accuracy.

Findings

Achieved 97.5% accuracy in curvature discrimination

Compared Naive Bayes, ANN, and SVM for optimal performance

Ridged skin structure outperforms flat designs in tactile sensing

Abstract

One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects' shapes and, more specifically, the objects' surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 \times 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Na\"ive Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared for various parameters. Finally, the most accurate method was selected to evaluate the proposed skin structure in recognition of three different curvatures. The results showed an accuracy rate of 97.5% in surface curvature discrimination.