Advertisement

A Bio-inspired Method for Incipient Slip Detection

  • Rosana Matuk Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4830)

Abstract

Few years old children lift and manipulate unfamiliar objects more dexterously than today’s robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. In a human dexterous manipulation a crucial event is the detection of incipient slips. Humans detect the incipient slips based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to detect the incipient slips using artificial neural networks that receive as input simulated human afferent responses. This method is strongly inspired on neurophysiological studies of the afferent responses during the human dexterous manipulation. Finite element analysis was used to model two fingers and an object, and simulated experiments using the proposed method were done. To the best of our knowledge, this is the first time that simulated human afferent signals are combined with finite element analysis and artificial neural networks, to detect the incipient slips.

Keywords

Neural networks Dexterous manipulation Robotics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Smith, A.: Some shear facts and pure friction related to roughness discrimination and the cutaneous control of grasping. Can. J. Physiol. Pharmacol. 72, 583–590 (1993)Google Scholar
  2. 2.
    Fagergren, A.: A multidisciplinary system identification of the human precision grip. PhD thesis, Karolinska Institutet, Stockholm (2003)Google Scholar
  3. 3.
    Johansson, R.: Tactile sensibility in the human hand: receptive field characteristics of mechanoreceptive units in the glabrous skin area. Journal of Physiology 281, 101–123 (1978)Google Scholar
  4. 4.
    Israelsson, A.: Simulation of responses in afferents from the glabrous skin during human manipulation. Master’s thesis, Master thesis in Cognitive Science, Umeå University, Umeå, Sweden (2002)Google Scholar
  5. 5.
    Johansson, R.: Sensory and memory information in the control of dexterous manipulation. In: Neural Bases of Motor Behaviour, pp. 205–260. Kluwer Academic Publishers, Dordrecht (1996)Google Scholar
  6. 6.
    Johnson, K.: The roles and functions of cutaneous mechanoreceptors. Curr. Opin. Neurobiol. 11, 455–461 (2001)CrossRefGoogle Scholar
  7. 7.
    Johansson, R., Westling, G.: Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip. Exp. Brain Res. 66, 141–154 (1987)CrossRefGoogle Scholar
  8. 8.
    Westling, G., Johansson, R.: Responses in glabrous skin mechanoreceptors during precision grip in humans. Exp. Brain Res. 66, 128–140 (1987)CrossRefGoogle Scholar
  9. 9.
    North, J., Gibson, F.: Volume compressibility of human abdominal skin. J. Biomech. 203–207 (1978)Google Scholar
  10. 10.
    Srinivasan, M., Gulati, R., Dandekar, K.: In vivo compressibility of the human fingertip. Adv. Bioeng. (22), 573–576 (1992)Google Scholar
  11. 11.
    Dandekar, K., Raju, B., Srinivasan, M.: 3-d finite-element models of human and monkey fingertips to investigate the mechanics of tactile sense. Transactions of the ASME 125, 682–691 (2003)CrossRefGoogle Scholar
  12. 12.
    Maeno, T., Kawamura, T., Cheng, S.: Friction estimation by pressing an elastic finger-shaped sensor against a surface. IEEE Transactions on Robotics and Automation 20(2), 222–228 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rosana Matuk Herrera
    • 1
  1. 1.Department of Computer Science, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos AiresArgentina

Personalised recommendations