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Tactile Sensing and Machine Learning for Human and Object Recognition in Disaster Scenarios

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ROBOT 2017: Third Iberian Robotics Conference (ROBOT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 694))

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Abstract

This paper presents the application of machine learning to tactile sensing for rescue robotics. Disaster situations often exhibit low-visibility scenarios where haptic feedback provides a valuable information for the search of potential victims. To extract haptic information from the environment, a tactile sensor attached to a lightweight robotic arm is used. Then, methods based on the SURF descriptor, support vector machines (SVM), Deep Convolutional Neural Networks (DCNN) and transfer learning are implemented to classify the data. Besides, experiments have been carried out, to compare those procedures, using different contact elements, such as human parts and objects that could be found in catastrophe scenarios. The best achieved accuracy of \(92.22\%\), results from the application of the transfer learning procedure using a pre-trained DCNN and fine-tuning the classification layer of the network.

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Acknowledgment

This work was partially supported by the Spanish project DPI2015-65186-R and the European Commission under grant agreement BES-2016-078237.

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Authors and Affiliations

  1. System Engineering and Automation Department, University of Málaga, 29071, Málaga, Spain

    Juan M. Gandarias, Jesús M. Gómez-de-Gabriel & Alfonso J. García-Cerezo

Authors
  1. Juan M. Gandarias
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  2. Jesús M. Gómez-de-Gabriel
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  3. Alfonso J. García-Cerezo
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Corresponding author

Correspondence to Juan M. Gandarias .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain

    Anibal Ollero

  2. Institut de Robòtica I Informàtica Industrial (CSIC-UPC), Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Sanfeliu

  3. Departamento de Informática e Ingeniería de Sistemas, Escuela de Ingeniería y Arquitectura, Instituto de Investigación en Ingeniería de Aragón, Zaragoza, Spain

    Luis Montano

  4. Institute of Electronics and Telematics Engineering of Aveiro (IEETA), Universidade de Aveiro, Aveiro, Portugal

    Nuno Lau

  5. IDMEC, Instituto Superior Técnico de Lisboa, Universidade de Lisboa, Lisbon, Portugal

    Carlos Cardeira

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Gandarias, J.M., Gómez-de-Gabriel, J.M., García-Cerezo, A.J. (2018). Tactile Sensing and Machine Learning for Human and Object Recognition in Disaster Scenarios. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-70836-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70835-5

  • Online ISBN: 978-3-319-70836-2

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