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Sensors 2017, 17(6), 1187; doi:10.3390/s17061187

Multimodal Bio-Inspired Tactile Sensing Module for Surface Characterization

1
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
2
Department of Computer Science and Engineering, Université du Quebec en Outaouais, Gatineau, QC J8X 3X7, Canada
This paper is an extended version of Alves de Oliveira, T.E.; Rocha Lima, B.M.; Cretu, A.; Petriu, E. Tactile profile classification using a multimodal MEMs-based sensing module. In Proceedings of the 3rd International Electronic Conference on Sensors and Applications, online, 15–30 November 2016; Volume 3, p. E007.
*
Author to whom correspondence should be addressed.
Academic Editor: Dirk Lehmhus
Received: 31 March 2017 / Revised: 12 May 2017 / Accepted: 17 May 2017 / Published: 23 May 2017
View Full-Text   |   Download PDF [6117 KB, uploaded 23 May 2017]   |  

Abstract

Robots are expected to recognize the properties of objects in order to handle them safely and efficiently in a variety of applications, such as health and elder care, manufacturing, or high-risk environments. This paper explores the issue of surface characterization by monitoring the signals acquired by a novel bio-inspired tactile probe in contact with ridged surfaces. The tactile module comprises a nine Degree of Freedom Microelectromechanical Magnetic, Angular Rate, and Gravity system (9-DOF MEMS MARG) and a deep MEMS pressure sensor embedded in a compliant structure that mimics the function and the organization of mechanoreceptors in human skin as well as the hardness of the human skin. When the modules tip slides over a surface, the MARG unit vibrates and the deep pressure sensor captures the overall normal force exerted. The module is evaluated in two experiments. The first experiment compares the frequency content of the data collected in two setups: one when the module is mounted over a linear motion carriage that slides four grating patterns at constant velocities; the second when the module is carried by a robotic finger in contact with the same grating patterns while performing a sliding motion, similar to the exploratory motion employed by humans to detect object roughness. As expected, in the linear setup, the magnitude spectrum of the sensors’ output shows that the module can detect the applied stimuli with frequencies ranging from 3.66 Hz to 11.54 Hz with an overall maximum error of ±0.1 Hz. The second experiment shows how localized features extracted from the data collected by the robotic finger setup over seven synthetic shapes can be used to classify them. The classification method consists on applying multiscale principal components analysis prior to the classification with a multilayer neural network. Achieved accuracies from 85.1% to 98.9% for the various sensor types demonstrate the usefulness of traditional MEMS as tactile sensors embedded into flexible substrates. View Full-Text
Keywords: tactile sensing; MARG system; MEMS; robotic probe; surface profile classification tactile sensing; MARG system; MEMS; robotic probe; surface profile classification
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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).

Supplementary material

  • Externally hosted supplementary file 1
    Doi: 10.3390/ecsa-3-E007
    Link: http://sciforum.net/conference/ecsa-3/paper/3755
    Description: The paper entitled "Tactile profile classification using a multimodal MEMs-based sensing module" was the initial paper submitted to The 3rd International Electronic Conference on Sensors and Applications (ECSA 2016).

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MDPI and ACS Style

Alves de Oliveira, T.E.; Cretu, A.-M.; Petriu, E.M. Multimodal Bio-Inspired Tactile Sensing Module for Surface Characterization . Sensors 2017, 17, 1187.

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