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One of the fundamental requirements for an artificial hand to successfullygrasp 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 embeddedtactile sensors by proposing a ridged fingertip structure, simulating humanfingerprints. In addition, a curvature detection approach based on machinelearning methods is proposed to provide the embedded sensors with the abilityto discriminate the surface curvature of different objects. For this purpose, aset of experiments were carried out to collect tactile signals from a 2 \times2 tactile sensor array, then the signals were processed and used for learningalgorithms. To achieve the best possible performance for our machine learningapproach, three different learning algorithms of Na\"ive Bayes (NB), ArtificialNeural Networks (ANN), and Support Vector Machines (SVM) were implemented andcompared for various parameters. Finally, the most accurate method was selectedto evaluate the proposed skin structure in recognition of three differentcurvatures. The results showed an accuracy rate of 97.5% in surface curvaturediscrimination.

Artificial Skin Ridges Enhance Local Tactile Shape Discrimination