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Multi-IMF Sample Entropy Features with Machine Learning for Surface Texture Recognition Based on Robot Tactile Perception
International Journal of Humanoid Robotics ( IF 1.5 ) Pub Date : 2021-04-17 , DOI: 10.1142/s0219843621500055
Shiliang Shao 1, 2 , Ting Wang 1, 2 , Yun Su 1, 2 , Chen Yao 1, 2 , Chunhe Song 1, 2 , Zhaojie Ju 1, 2, 3
Affiliation  

Discrimination of surface textures using tactile sensors has attracted increasing attention. Intelligent robotics with the ability to recognize and discriminate the surface textures of grasped objects are crucial. In this paper, a novel method for surface texture classification based on tactile signals is proposed. For the proposed method, first, the tactile signals of each channel (X, Y, Z, and S) are decomposed based on empirical mode decomposition (EMD). Then, the intrinsic mode functions (IMFs) are obtained. Second, based on the multiple IMFs, the sample entropy is calculated for each IMF. Therefore, the multi-IMF sample entropy (MISE) features are obtained. Last but not least, based on the two public datasets, a variety of machine learning algorithms are used to recognize different textures. The results show that the SVM classification method, with the proposed MISE features, achieves the highest classification accuracy. Undeniably, the MISE features with the SVM method, proposed in this paper, provide a novel idea for the recognition of surface texture based on tactile perception.

中文翻译:

基于机器学习的多IMF样本熵特征用于基于机器人触觉感知的表面纹理识别

使用触觉传感器识别表面纹理已引起越来越多的关注。能够识别和区分抓取物体表面纹理的智能机器人至关重要。本文提出了一种基于触觉信号的表面纹理分类新方法。对于所提出的方法,首先,基于经验模式分解(EMD)分解每个通道(X、Y、Z 和 S)的触觉信号。然后,获得固有模式函数(IMF)。其次,基于多个 IMF,计算每个 IMF 的样本熵。因此,获得了多IMF样本熵(MISE)特征。最后但同样重要的是,基于这两个公共数据集,各种机器学习算法用于识别不同的纹理。结果表明,SVM分类方法,使用提出的 MISE 特征,实现了最高的分类精度。不可否认,本文提出的基于 SVM 方法的 MISE 特征为基于触觉感知的表面纹理识别提供了一种新思路。
更新日期:2021-04-17
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