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Article

Texture Identification of Objects Using a Robot Fingertip Module with Multimodal Tactile Sensing Capability

by 1,2, 1 and 1,2,*
1
Group for Mechanical Metrology, Division of Physical Metrology, Korea Research Institute of Standards and Science, 267 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
2
Department of Science of Measurement, University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Manuel Armada
Appl. Sci. 2021, 11(11), 5256; https://doi.org/10.3390/app11115256
Received: 30 April 2021 / Revised: 27 May 2021 / Accepted: 2 June 2021 / Published: 5 June 2021
(This article belongs to the Special Issue Haptics: Technology and Applications)
Modern robots fall behind humans in terms of the ability to discriminate between textures of objects. This is due to the fact that robots lack the ability to detect the various tactile modalities that are required to discriminate between textures of objects. Hence, our research team developed a robot fingertip module that can discriminate textures of objects via direct contact. This robot fingertip module is based on a tactile sensor with multimodal (3-axis force and temperature) sensing capabilities. The multimodal tactile sensor was able to detect forces in the vertical (Z-axis) direction as small as 0.5 gf and showed low hysteresis error and repeatability error of less than 3% and 2% in the vertical force measurement range of 0–100 gf, respectively. Furthermore, the sensor was able to detect forces in the horizontal (X- and Y-axes) direction as small as 20 mN and could detect 3-axis forces with an average cross-talk error of less than 3%. In addition, the sensor demonstrated its multimodal sensing capability by exhibiting a near-linear output over a temperature range of 23–35 °C. The module was mounted on a motorized stage and was able to discriminate 16 texture samples based on four tactile modalities (hardness, friction coefficient, roughness, and thermal conductivity). View Full-Text
Keywords: robot fingertip; texture discrimination; multi-modal; tactile sensor robot fingertip; texture discrimination; multi-modal; tactile sensor
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MDPI and ACS Style

Bok, B.-G.; Jang, J.-S.; Kim, M.-S. Texture Identification of Objects Using a Robot Fingertip Module with Multimodal Tactile Sensing Capability. Appl. Sci. 2021, 11, 5256. https://doi.org/10.3390/app11115256

AMA Style

Bok B-G, Jang J-S, Kim M-S. Texture Identification of Objects Using a Robot Fingertip Module with Multimodal Tactile Sensing Capability. Applied Sciences. 2021; 11(11):5256. https://doi.org/10.3390/app11115256

Chicago/Turabian Style

Bok, Bo-Gyu, Jin-Seok Jang, and Min-Seok Kim. 2021. "Texture Identification of Objects Using a Robot Fingertip Module with Multimodal Tactile Sensing Capability" Applied Sciences 11, no. 11: 5256. https://doi.org/10.3390/app11115256

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