Wang Si-Ao, Albini Alessandro, Maiolino Perla, Mastrogiovanni Fulvio, Cannata Giorgio
MACLAB, Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università degli Studi di Genova, Genoa, Italy.
Oxford Robotics Institute, University of Oxford, Oxford, United Kingdom.
Front Neurorobot. 2022 Feb 23;16:808222. doi: 10.3389/fnbot.2022.808222. eCollection 2022.
Tactile sensing endows the robots to perceive certain physical properties of the object in contact. Robots with tactile perception can classify textures by touching. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors can also be identified through exploratory robotic movements like sliding. To study the problem of fine texture classification, we design a robotic sliding experiment using a finger-shaped multi-channel capacitive tactile sensor. A feature extraction process is presented to encode the acquired tactile signals (in the form of time series) into a low dimensional (≤7D) feature vector. The feature vector captures the frequency signature of a fabric texture such that fabrics can be classified directly. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, to investigate the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. Instead, variation of sliding speed shows no apparent effects. In summary, this paper presents a study of texture classification on fabrics by training a simple k-NN classifier, using only one modality and one type of exploratory motion (sliding). The classification accuracy can reach up to 96%. The analysis of the feature space also implies a potential parametric representation of textures for tactile perception, which could be used for the adaption of motion to reach better classification performance.
触觉传感使机器人能够感知接触物体的某些物理特性。具有触觉感知能力的机器人可以通过触摸对纹理进行分类。有趣的是，通过像滑动这样的探索性机器人运动，还可以识别超出触觉传感器标称分辨率的精细微观几何纹理。为了研究精细纹理分类问题，我们使用手指形状的多通道电容式触觉传感器设计了一个机器人滑动实验。提出了一种特征提取过程，将采集到的触觉信号（以时间序列的形式）编码为低维（≤7维）特征向量。该特征向量捕获织物纹理的频率特征，从而可以直接对织物进行分类。实验包括滑动参数（即速度和压力）的多种组合，以研究滑动参数与生成的特征空间之间的相关性。结果表明，改变接触压力会极大地影响提取特征向量的显著性。相反，滑动速度的变化没有明显影响。总之，本文通过训练一个简单的k近邻分类器，仅使用一种模态和一种探索性运动（滑动），对织物纹理分类进行了研究。分类准确率可达96%。对特征空间的分析还暗示了一种潜在的纹理参数表示用于触觉感知，可用于调整运动以获得更好的分类性能。
