Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-06-04T20:26:56.468Z Has data issue: false hasContentIssue false
  • English
  • Français

Stochastic resonance aided tactile sensing

Published online by Cambridge University Press:  01 July 2009

Masahiro Ohka  and
Shingo Kondo
Masahiro Ohka*
Affiliation:
Department of Complex Systems Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
Shingo Kondo
Affiliation:
Mitsubishi Electric Corporation, 2-7-3, Marunouchi, Chiyoda-ku, Tokyo, 100-8310, Japan
*
*Corresponding author: E-mail: ohka@is.nagoya-u.ac.jp
Get access Rights & Permissions [Opens in a new window]

Summary

Stochastic resonance (SR) is one of the basic principles intrinsically possessed by any living thing to highly adapt to complicated environments including various disturbances. Although a noise is inevitably mixed by contact with an object and a sensor's movement on it in tactile sensing, a human being can evaluate the several micrometers of unevenness on the object surface by means of SR. We intend to apply SR to tactile sensing and to develop a tactile sensing system capable of measuring an object surface with high precision in not only a controlled environment like a precision measurement room but also in a living environment. First, we investigate the SR characteristic possessed by a Hodgkin–Huxley model capable of emulating squid's neuron activities. According to the simulation results, we develop a new electronic circuit capable of generating the SR. We perform the object surface scan using a linear stage equipped with a tactile sensor and the circuit. A series of object surface scanning tests is repeated while changing the intensity of applied noise, and the signal to noise ratio (SNR, hereafter) is calculated from the obtained measurement data to check the effect of the SR. In the experiment, striped textures with a height of δ = 5 ~ 30 μm are used as specimens. The SNR changes depending on the noise intensity, and the local maximum appears under a proper noise. It is found that the sensing accuracy is improved according to the aforementioned SR theory. Therefore, SR, which is usually applied to noisy environments, is effective for a tactile sensing system.

Keywords

Tactile sensorComplex systems scienceStochastic resonanceHodgkin–Huxley modelBio-mimetic processing
Type
Article
Information
Robotica , Volume 27 , Issue 4 , July 2009 , pp. 633 - 639
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Moss, F. and Wiesenfeld, K., “The benefits of background noise,” Nikkei Science (Oct. 1995) 126–131 (translated to Japanese).Google Scholar
2.Gammaitoni, L., Hänggi, P., Jung, P. and Marchesoni, F., “Stochastic resonance,” Rev. Mod. Phys. 70 (1), 223–287 (1998).CrossRefGoogle Scholar
3.Kanamaru, T., Horita, T. and Okabe, Y., “Stochastic resonance in the Hodgkin-Huxley network,” J. Phys. Soc. Japan 67 (12), 4058–4063 (1998).CrossRefGoogle Scholar
4.Chik, D. T. W., Wang, Y. and Wang, Z. D., “Stochastic resonance in a Hodgkin-Huxley neuron in the absence of external noise,” Phys. Rev. 64 (2001), File No.: 021913.Google Scholar
5.Schmid, G., Goychuk, I. and Hänggi, P., “Stochastic resonance as a collective property of ion channel assemblies,” Europhys. Letters 56 (1), 22–28 (2001).CrossRefGoogle Scholar
6.Sakumaru, Y. and Aihara, K., “Stochastic resonance and coincidence detection in single neurons,” Neural Process. Letters 16, 235–242 (2002).CrossRefGoogle Scholar
7.Gluckman, B. J., Netoff, T. I., Neel, E. J., Ditto, W. L., Spano, M. L. and Schiff, S. J., “Stochastic resonance in a neuronal network from mammalian brain,” Phys. Rev. Letters 77 (19), 4098–4101 (1996).CrossRefGoogle Scholar
8.Collins, J. J., Thomas, T. Imhoff and Grigg, P., “Noise-mediated enhancements and decrements in human tactile sensation,” Phys. Rev. E 56 (1), 923–926 (1997).CrossRefGoogle Scholar
9.Miyaoka, T. and Ohka, M., “Tactile Information Processing Mechanisms of Fine Surface Texture Discrimination in Humans: A Study with Ridge Height Discrimination Tasks,” International Ergonomics Association 13th Triennial Congress of the IEA 7, 264–266 (1997).Google Scholar
10.Miyaoka, T., Mano, T. and Ohka, M., “Mechanisms of fine-surface-texture discrimination in human tactile sensation,” J. Acoust. Soc. America 105 (4), 2485–2492 (1999).CrossRefGoogle ScholarPubMed
11.Ohka, M., Kawamura, T., Itahashi, T., Miyaoka, T. and Mitsuya, Y., “A tactile recognition system mimicking human mechanism for recognizing surface roughness,” JSME Int. J., Series C. 48 (2), 278–285 (2005).CrossRefGoogle Scholar
12.Ohka, M., Takayanagi, J., Kawamura, T. and Mitsuya, Y., “A surface-shape recognition system mimicking human mechanism for tactile sensation,” Robotica 24, 595–602 (2006).CrossRefGoogle Scholar
13.Hodgkin, A. L. and Huxley, A. F., “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol 117, 500–544 (1952).CrossRefGoogle ScholarPubMed