Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger
Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger
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DOI：
10.1109/sensors43011.2019.8956704
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发表时间：
2019-10
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影响因子：
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通讯作者：
Sriramana Sankar;Alisa Brown;D. Balamurugan;Harrison H. Nguyen;Mark M. Iskarous;Talya Simcox;Deepesh Kumar;Andrei Nakagawa Silva;N. Thakor
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文献类型：
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作者：
Sriramana Sankar;Alisa Brown;D. Balamurugan;Harrison H. Nguyen;Mark M. Iskarous;Talya Simcox;Deepesh Kumar;Andrei Nakagawa Silva;N. Thakor
柔软的机器人手指在与环境互动时提供了增强的灵活性和灵巧性。通过将软手指与触觉传感器相结合来区分不同的纹理表面，可以进一步提高软手指的能力。在这项工作中，一个柔性的3×3织物触觉传感器阵列与一个柔软的仿生手指集成在一起，用于纹理识别任务。手指触摸了七个不同质地的板，相应的触觉反应被转化为神经形态的尖峰模式，模仿皮肤中机械感受器的放电模式。利用支持向量机(SVM)分类器，利用基于特征的特征度量对不同纹理进行分类。当每个紫杉醇的平均穗率和平均穗间距作为分类器的输入时，传感器能够达到99.21%的准确率。实验表明，一个廉价、柔软的仿生手指与灵活的触觉传感器阵列相结合，可能会帮助用户更好地感知他们的环境。
Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3×3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better.