Material recognition using tactile sensing
Material recognition using tactile sensing
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DOI：
10.1016/j.eswa.2017.10.045
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发表时间：
2018-03-15
影响因子：
8.5
通讯作者：
Coleman, Sonya
中科院分区：
文献类型：
--
作者：
Kerr, Emmett;McGinnity, T. M.;Coleman, Sonya
识别制造物体的材料对于有效的机器人抓取和操作具有重要价值。材料的特性可以使用不同的感官模式来检索：基于视觉的，基于触觉的或基于声音的。可压缩性、表面纹理和热性能都可以通过使用触觉传感器从与物体的物理接触中获得。本文提出了一种利用仿生指尖与各种材料接触来收集数据的方法，然后利用这些数据对材料进行单独和分组分类。在获取数据后，使用主成分分析（PCA）提取特征。这些特征被用来训练七种不同的分类器和这些分类器的混合结构进行比较。对于所有材料，人工系统都被相互评估，并与人类的表现进行比较，结果发现所有人工系统的表现都优于人类参与者的平均表现。这些结果突出了BioTAC传感器的敏感性，并为需要敏感和准确方法的研究铺平了道路，例如使用机器人系统进行生命体征监测。爱思唯尔有限公司2017年版权所有版权所有。
Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants' average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems. Crown Copyright (C) 2017 Published by Elsevier Ltd. All rights reserved.