标题： 人工皮肤脊增强局部触觉形状辨别 显示英文标题

标题： Artificial Skin Ridges Enhance Local Tactile Shape Discrimination

摘要： 一个人工手要成功抓取和操作物体，基本要求之一是能够区分不同物体的形状，更具体地说，是能够区分物体的表面曲率。 在本研究中，我们探讨了通过提出一种带有脊状指尖结构来增强嵌入式触觉传感器的曲率检测的可能性，这种结构模拟了人类指纹。 此外，提出了一种基于机器学习方法的曲率检测方法，以使嵌入式传感器具备区分不同物体表面曲率的能力。 为此，进行了一组实验，从一个2\times 2触觉传感器阵列中收集触觉信号，然后对这些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，选择了最准确的方法来评估所提出的皮肤结构在识别三种不同曲率方面的表现。 结果表明，在表面曲率区分中的准确率为97.5%。 显示英文摘要

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 \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.