Artificial Skin Ridges Enhance Local Tactile Shape Discrimination

Sensors

Saba Salehi¹  John-John Cabibihan² 

[1] Social Robotics Lab, Interactive and Digital Media Institute (IDMI), Department of Electrical and Computer Engineering, National University of Singapore, 21 Heng Mui Keng Terrace, 119613 Singapore;

关键词: tactile sensing;    curvature discrimination;    local shape;    ridged skin cover;    fingerprints;    robotic hand;    prosthetic hand;    machine learning;    support vector machines;   

DOI  :  10.3390/s110908626

来源: mdpi

【 摘 要 】

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 × 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 Naïve 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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.