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Real-Time Slip Detection and Control Using Machine Learning

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XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 83))

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Abstract

The handling and gripping of objects by a prosthesis depend on the precise applied force control and the slip detection of the grasped object. These two features combined allow for the adjustment of the minimum grip force required to prevent slipping. Based on this statement, a system was developed to control the slip of objects, composed of a grip controller, for which the objective was to hold the object, and through the signal of a tactile sensor, slip is detected. An artificial neural network was used to identify the slip event for different types of objects. If the response from the classifier is positive, indicating slip, the system sends a signal to the grip controller, so that it increases the grip force performed on the object, aiming at minimizing slippage. In the end, the performance of the system for different objects was analyzed; the result encountered was that the system efficiency is proportional to the mass and the rigidity of the grasped object.

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Acknowledgements

The authors would like to thank the School of Electric Engineering, Federal University of Uberlândia (FEELT-UFU). Also, they are thankful for the financial support provided to the present research effort by CAPES.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Federal University of Uberlândia, Av. João Naves de Ávila, 2121, 38408-196, Uberlândia, Brazil

    Alexandre Henrique Pereira Tavares & S. R. J. Oliveira

Authors
  1. Alexandre Henrique Pereira Tavares
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  2. S. R. J. Oliveira
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Corresponding author

Correspondence to Alexandre Henrique Pereira Tavares .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Teodiano Freire Bastos-Filho

  2. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Eliete Maria de Oliveira Caldeira

  3. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Anselmo Frizera-Neto

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Tavares, A.H.P., Oliveira, S.R.J. (2022). Real-Time Slip Detection and Control Using Machine Learning. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_202

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  • DOI: https://doi.org/10.1007/978-3-030-70601-2_202

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70600-5

  • Online ISBN: 978-3-030-70601-2

  • eBook Packages: EngineeringEngineering (R0)

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