基于压电薄膜传感器的机器人触觉识别系统

为了使机器人通过触觉感知外部环境信息，弥补视听交互信息缺失的不足，根据聚偏氟乙烯（PVDF）材料的压电效应设计开发基于触觉传感器和卷积神经网络的机器人触觉识别系统，能够根据所采集的触觉信号识别出材质类型. 提出基于渐进式级联卷积神经网络的触觉识别算法. 该算法基于卷积神经网络提取机器人传感器的信号特征，包括经过短时傅里叶变换的触觉数据频谱图和信号表征周期内的时域特征. 为了解决特定材质识别混淆的问题，利用K-Medoids聚类算法和动态时间规整（DTW）距离度量算法将分类过程区分为粗、细2个层次，构建渐进式分类模型. 实验表明，设计的触觉传感器对物体材质的平均识别正确率约为97%，机器人能够成功识别触摸到的真实材质，为下一步的探索交互任务奠定基础.

Robot tactile recognition system based on piezoelectric film sensor

A robot tactile recognition system based on tactile sensor and convolutional neural network was designed and developed according to the piezoelectric effect of polyvinylidene fluoride (PVDF) material in order to make the robot sense the external environment information through touch and make up for the lack of audio-visual interaction information. The material type can be identified according to the collected tactile signal. A tactile recognition algorithm was proposed based on the progressive cascade convolutional neural network. The signal features of the robot sensor were extracted based on the convolutional neural network, including the short-time Fourier transform of the tactile data spectrum and the time domain characteristics of the signal representation period. The classification process was divided into coarse and fine levels by K-Medoids clustering algorithm and dynamic time warping (DTW) distance measure algorithm in order to solve the confusion problem of specific material recognition. The progressive classification model was constructed. The experimental results showed that the average recognition accuracy of the tactile sensor was about 97%. The robot can successfully recognize the touched real material, laying a foundation for the next exploration and interaction tasks.