Artificial Skin Ridges Enhance Local Tactile Shape Discrimination
AbstractOne 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.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Salehi, S.; Cabibihan, J.-J.; Ge, S.S. Artificial Skin Ridges Enhance Local Tactile Shape Discrimination. Sensors 2011, 11, 8626-8642.
Salehi S, Cabibihan J-J, Ge SS. Artificial Skin Ridges Enhance Local Tactile Shape Discrimination. Sensors. 2011; 11(9):8626-8642.Chicago/Turabian Style
Salehi, Saba; Cabibihan, John-John; Ge, Shuzhi Sam. 2011. "Artificial Skin Ridges Enhance Local Tactile Shape Discrimination." Sensors 11, no. 9: 8626-8642.