Social Robotics Lab, Interactive and Digital Media Institute, Department of Electrical and Computer Engineering, National University of Singapore, 21 Heng Mui Keng Terrace, 119613 Singapore.
Sensors (Basel). 2011;11(9):8626-42. doi: 10.3390/s110908626. Epub 2011 Sep 5.
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.
为了使假肢能够成功地抓取和操纵物体，其中一个基本要求是能够区分不同物体的形状，更具体地说，是区分物体的表面曲率。在这项研究中，我们通过提出一种模拟人类指纹的脊状指尖结构来研究增强嵌入式触觉传感器曲率检测的可能性。此外，还提出了一种基于机器学习方法的曲率检测方法，为嵌入式传感器提供区分不同物体表面曲率的能力。为此，进行了一组实验来从 2×2 触觉传感器阵列中收集触觉信号，然后对信号进行处理并用于学习算法。为了使我们的机器学习方法达到最佳性能，实现了三种不同的学习算法：朴素贝叶斯 (NB)、人工神经网络 (ANN) 和支持向量机 (SVM)，并针对各种参数进行了比较。最后，选择最准确的方法来评估所提出的用于识别三种不同曲率的皮肤结构。结果表明，在表面曲率识别方面的准确率达到了 97.5%。
