Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Recognition of Tactile Attribute Strength and Category Using Convolutional Neural Network

Version 1 : Received: 22 July 2021 / Approved: 23 July 2021 / Online: 23 July 2021 (09:27:35 CEST)

How to cite: Zhang, P.; Yu, G.; Shan, D.; Chen, Z.; Wang, X. Recognition of Tactile Attribute Strength and Category Using Convolutional Neural Network. Preprints 2021, 2021070530 (doi: 10.20944/preprints202107.0530.v1). Zhang, P.; Yu, G.; Shan, D.; Chen, Z.; Wang, X. Recognition of Tactile Attribute Strength and Category Using Convolutional Neural Network. Preprints 2021, 2021070530 (doi: 10.20944/preprints202107.0530.v1).

Abstract

Objectives: In order to solve the problem that most of the existing research focuses on the binary tactile attributes of objects,which ignores the tactile attribute strength and category recognition,an attribute strength and category recognition method based on convolutional neural network matrix-label is proposed. Methods:Firstly,in the data preparation stage,we preprocess the raw data and determine the matrix labels to build the haptic dataset.Secondly,in the feature extraction stage,we fuse the haptic data of two fingers and use the convolutional neural network to extract the attribute strength features.Finally,in the attribute strength and category recognition stage,all channel haptic data is fused to predict the attribute strength and category.Results:We compared with the multi-label convolutional neural network method in terms of elastic strength,hardness strength and category,and compared the attribute strength recognition capabilities of the two methods using novel objects outside the haptic dataset.The results show that the accuracy of the last 20 iterations of the matrix-label method has an average elastic strength of 96.73%,hardness strength of 97.34%,and category of 96.67%.The performance is better.When the Euclidean distance between the prediction of the novel object and the real label is less than 1,the accuracy of the elastic strength is best to reach 100%,and the hardness strength is best to reach 100%.The performance is better. Conclusions:The effectiveness of the method has been verified.Comparing with the convolutional neural network method,our method can effectively recognize the attribute strength and category of objects.

Keywords

robot tactile; convolution neural network; attribute strength identification; category identification; robot operating system

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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