基于振动加速度信息的触觉纹理分类方法研究  

Research on the tactile texture classification method based on vibration acceleration information

作　　者：陈大鹏 朱栋梁 刘佳[1,2,3] 宋爱国 陈庚 Chen Dapeng;Zhu Dongliang;Liu Jia;Song Aiguo;Chen Geng(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044,China;Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot(C-IMER),Nanjing 210044,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing 210044,China;School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China)

机构地区：[1]南京信息工程大学自动化学院,南京210044 [2]江苏省智能气象探测机器人工程研究中心,南京210044 [3]江苏省大气环境与装备技术协同创新中心,南京210044 [4]东南大学仪器科学与工程学院,南京210096

出　　处：《仪器仪表学报》2023年第5期121-130,共10页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(62003169,61773219);江苏省自然科学基金青年基金(BK20200823);江苏省高等学校自然科学研究项目(20KJB520029);江苏省研究生科研与实践创新计划项目(SJCX22_0348)资助。

摘　　要：当手持刚性工具在材料表面滑动时,用户可以通过工具的振动来感受材料表面的纹理特征。这些振动加速度数据包含了丰富的纹理类别信息,为纹理的分类提供了基础。利用触觉进行纹理分类对于力触觉人机交互、机器人精细化操作等应用具有重要的意义。目前,手工设计与纹理相关的特征以及借助卷积神经网络进行简单的特征提取等方法已经被应用于触觉纹理分类。然而,这些方法未能关注时间尺度的选择和触觉序列数据间的时间依赖性,还存在触觉数据特征提取不充分和分类精度不佳等问题。为了解决上述问题,本文提出一种由多尺度卷积网络和双向长短时记忆网络相结合的融合模型,以便同时捕获触觉信号多尺度的几何局部空间特征和时间依赖特征。所提出的模型从公开的触觉数据集中学习材料表面纹理的触觉特征,并在公开的纹理振动加速度数据库上进行训练。实验结果表明,本文提出的模型可以稳健且高效地实现最高92.1%的纹理分类精度。When a user holds a rigid tool to slide on the material surface,the texture features of the material surface through vibration of the tool is felt.These vibration acceleration data contain rich texture category information,which provides a basis for texture classification.Texture classification based on tactile sense is of great significance for applications such as haptic human-computer interaction and fine manipulation of robots.At present,the methods of manually designing features related to texture and simple feature extraction using convolutional neural network have been applied to tactile texture classification.However,these methods fail to pay attention to the selection of time scale and the time dependence between tactile serial data,and there are still problems such as insufficient feature extraction of tactile data and poor classification accuracy.To solve the above problems,this article proposes a fusion model which combines multi-scale convolutional network and bidirectional long short memory network to capture multi-scale geometric local spatial features and time dependent features of tactile signals at the same time.The proposed model learns the tactile features of material surface texture from an open tactile data set,and trains them on the open texture vibration acceleration database.The experimental results show that the proposed model achieves the highest texture classification accuracy of 92.1% robustly and efficiently.

关 键 词：触觉纹理分类 深度学习 振动加速度数据 

分 类 号：TH7[机械工程—仪器科学与技术]