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A traction stress sensor array for use in high-resolution robotic tactile imaging

Kane, B.J., Cutkosky, M.R., Kovacs, G.T.A.·2000·Journal of Microelectromechanical Systems

Abstract

A high resolution large-area array capable of resolving the three independent components of a 2D triaxial contact stress profile has been developed. The array, composed of 4096 (64/spl times/64) individual stress sensing elements, was constructed with a fully CMOS-compatible fabrication process, allowing integration of the sensing structures with digital control circuitry. The individual array elements have been shown to demonstrate linear responses to both applied normal stress (1.59 mV/kPa, 0-35 kPa) and applied shear stress (0.32 mV/kPa, 0-60 kPa). A spatial resolution comparable to the spacing of the papillary ridges of the human dermis (/spl ap/300 /spl mu/m) has been achieved within the 1.92/spl times/1.92 cm active sensing area of the array. Descriptions of the sensor structure, the required signal conditioning, and the array architecture are presented in this paper. The results of electrical and mechanical characterization studies are also outlined.

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Metadata

Authors
Kane, B. J., Cutkosky, M. R., & Kovacs, G. T. A.
Venue
Journal of Microelectromechanical Systems
Published
2000-12
Volume
9
Issue
4
Pages
425-434
ISSN
  • 1057-7157 (ISSN-L)
  • 1941-0158
OpenAlex
W2154352415
Cited by
235
References
0
FWCI
3.03
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A traction stress sensor array for use in high-resolution robotic tactile imaging. https://app.cochat.ai/papers/10.1109/84.896763
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Kane, B. J., Cutkosky, M. R., & Kovacs, G. T. A. (2000). A traction stress sensor array for use in high-resolution robotic tactile imaging. Journal of Microelectromechanical Systems. https://doi.org/10.1109/84.896763

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.