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Proceedings 2017, 1(2), 27; doi:10.3390/ecsa-3-E007

Tactile Profile Classification Using a Multimodal MEMs-Based Sensing Module

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Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, QC J8X 3X7, Canada
2
The Military Institute of Engineering, Rio de Janeiro, RJ 22291-270, Brazil
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School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5,Canada
Presented at the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Available online: https://sciforum.net/conference/ecsa-3.
*
Author to whom correspondence should be addressed.
Published: 14 November 2016
Download PDF [1934 KB, uploaded 28 June 2017]

Abstract

Robots are expected to perform complex dexterous operations in a variety of applications such as health and elder care, manufacturing, or high-risk environments. In this context, the most important task is to handle objects, the first step being the ability to recognize objects and their properties by touch. This paper concentrates on the issue of surface recognition by monitoring the interaction between a tactile probe in contact with a surface. A sliding motion is performed by a robot finger (i.e., kinematic chain composed of 3 motors) carrying the tactile probe on its end. The probe comprises a 9-DOF MEMs MARG (Magnetic, Angular Rate, and Gravity) sensor and a deep MEMs pressure (barometer) sensor, both embedded in a flexible compliant structure. The sensors are placed such that, when the tip is rubbed over a surface, the MARG unit vibrates and the deep pressure sensor captures the overall normal force exerted. The tactile probe collects data over seven synthetic shapes (profiles). The proposed method to distinguish them, in frequency and time domain, consists of applying multiscale principal components analysis prior to the classification with a multilayer neural network. The achieved classification accuracies of 85.1% to 98.9% for the various sensor types demonstrate the usefulness of traditional MEMs as tactile sensors embedded into flexible substrates.
Keywords: tactile sensing; MARG system; robotic probe; surface profile classification tactile sensing; MARG system; robotic probe; surface profile classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Alves De Oliveira, T.E.; Lima, B.M.R.; Cretu, A.-M.; Petriu, E.M. Tactile Profile Classification Using a Multimodal MEMs-Based Sensing Module. Proceedings 2017, 1, 27.

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