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Texture Discrimination with Artificial Whiskers in the Robot-Rat Psikharpax

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Book cover Biomedical Engineering Systems and Technologies (BIOSTEC 2010)

Abstract

We describe a novel algorithm for texture discrimination which we tested on a robot using an artificial whisker system. Experiments on both fixed head and mobile platform have shown that this system is efficient and robust, with greater behavioral capacities than previous similar approaches, thus, demonstrating capabilities to complement or supply vision. Moreover, results tends to show that the length and number of whiskers may be an important parameter for texture discrimination. From a more fundamental point of view these results suggest that two currently opposing hypotheses to explain texture recognition in rats, namely the “kinetic signature hypothesis” and the “resonance hypothesis”, may be, in fact, complementary.

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References

  1. Carvell, G., Simons, D.: Biometric analyses of vibrissal tactile discimination in the rat. Journal of Neuroscience 10, 2638–2648 (1990)

    Google Scholar 

  2. Guic-Robles, E., Valdivieso, C., Guarjardo, G.: Rats can learn a roughness discrimination using only their vibrissal system. Behavioural Brain Research 31, 285–289 (1989)

    Article  Google Scholar 

  3. Brecht, M., Preilowski, B., Merzenich, M.: Functional architecture of the mystacial vibrissae. Behavioural Brain Research 84, 81–97 (1997)

    Article  Google Scholar 

  4. Krupa, D.J., Matell, M.S., Brisben, A.J., Oliviera, L.M., Nicolelis, M.A.L.: Behavioural properties of the trigeminal somatosensory system in rats performing whisker-dependent tactile discrimination. J. Neurosci., 5752–5763 (2001)

    Google Scholar 

  5. Petersen, R.S., Diamond, M.E.: Spatial-Temporal Distribution of Whisker-Evoked Activity in Rat Somatosensory Cortex and the Coding of Stimulus Location. J. Neurosci. 20, 6135–6143 (2000)

    Google Scholar 

  6. Hartmann, M.J.: Active sensing capabilities of the rat whisker system. Autonomous Robots 11, 249–254 (2001)

    Article  MATH  Google Scholar 

  7. Brooks, R.A.: A robot that walks: Emergent behaviors from a carefully evolved network. Technical Report AI MEMO 1091, MIT (1989)

    Google Scholar 

  8. Russell, R.A.: Object recognition using articulated whisker probes. In: Proc. 15th Int. Symp. Industr. Robots, pp. 605–612 (1985)

    Google Scholar 

  9. Chapman, T., Hayes, A., Tilden, T.: Reactive maze solving with a biologically-inspired wind sensor. In: Meyer, J., Berthoz, A., Floreano, D., Roitblat, H., Wilson, S. (eds.) From Animals to Animats 6. Proc. of the 6th Int. Conf. on Simulation of Adaptive Behavior, pp. 81–87. MIT PRESS, A Bradford Book (2000)

    Google Scholar 

  10. Fend, M., Bovet, S., Yokoi, H., Pfeifer, R.: An active artificial whisker array for texture discrimination. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. II, pp. 1044–1049 (2003)

    Google Scholar 

  11. Lungarella, M., Hafner, V., Pfeifer, R., Yokoi, H.: Artificial whisker sensors in robotics. In: IEEE/RSJ International Conference on Intelligent Robots and System, vol. 3, pp. 2931–2936 (2002)

    Google Scholar 

  12. Kim, D., Moller, R.: A biomimetic whisker for texture discrimination and distance estimation. From Animals to Animats 8, 140–149 (2004)

    Google Scholar 

  13. Seth, A.K., McKinstry, J.L., Edelman, G.M., Krichmar, J.L.: Spatiotemporal processing of whisker input supports texture discrimination by a brain-based device. In: Schall, S., Ijspeert, A., Billard, A., Vijayakumar, S., Hallam, J., Meyer, J. (eds.) From Animals to Animats 8. Proc. of the 8th Int. Conf. on Simulation of Adaptive Behavior. MIT Press, MA (2004)

    Google Scholar 

  14. Fox, C.W., Mitchinson, B., Pearson, M.J., Pipe, A.G., Prescott, T.J.: Contact type dependency of texture classification in a whiskered mobile robot. Autonomous Robots (2009) (in press)

    Google Scholar 

  15. Meyer, J.A., Guillot, A., Girard, B., Khamassi, M., Pirim, P., Berthoz, A.: The psikharpax project: Towards building an artificial rat. Robotics and Autonomous Systems 50, 211–223 (2005)

    Article  Google Scholar 

  16. N’Guyen, S., Pirim, P., Meyer, J.A.: Elastomer-based tactile sensor array for the artificial rat psikharpax. In: ISEF 2009 - XIV International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (2009) (in press)

    Google Scholar 

  17. Arabzadeh, E., Panzeri, S., Diamond, M.E.: Whisker Vibration Information Carried by Rat Barrel Cortex Neurons. J. Neurosci. 24, 6011–6020 (2004)

    Article  Google Scholar 

  18. Arabzadeh, E., Zorzin, E., Diamond, M.E.: Neuronal encoding of texture in the whisker sensory pathway. PLoS Biol. 3, e17 (2005)

    Article  Google Scholar 

  19. Ghitza, O.: Auditory models and human performance in tasks related to speech coding and speech recognition. IEEE Transactions on Speech and Audio Processing 2, 115–132 (1994)

    Article  Google Scholar 

  20. Kim, D.S., Lee, S.Y., Kil, R.M.: Auditory processing of speech signals for robust speech recognition in real-world noisy environments. IEEE Transactions on Speech and Audio Processing 7, 55–69 (1999)

    Article  Google Scholar 

  21. Sreenivas, T.V., Niederjohn, R.J.: Spectral analysis for formant frequency estimation in noise. IEEE Transactions on Signal Processing 40, 282–293 (1992)

    Article  Google Scholar 

  22. Licklider, J.C.R., Pollack, I.: Effect of differentiation, integration, and infinite peak clipping upon the intelligibility of speech. Journal of the Acoustical Society of America 20, 42–52 (1948)

    Article  Google Scholar 

  23. Hipp, J., Arabzadeh, E., Zorzin, E., Conradt, J., Kayser, C., Diamond, M.E., Konig, P.: Texture Signals in Whisker Vibrations. J. Neurophysiol. 95, 1792–1799 (2006)

    Article  Google Scholar 

  24. Nissen, S.: Implementation of a Fast Artificial Neural Network Library (fann). Report, Department of Computer Science University of Copenhagen (DIKU) 31 (2003)

    Google Scholar 

  25. Igel, C., Hüskel, M.: Improving the rprop learning algorithm. In: Proceedings of the Second International Symposium on Neural Computation, NC 2000, pp. 115–121 (2000)

    Google Scholar 

  26. Fend, M.: Whisker-based texture discrimination on a mobile robot. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 302–311. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  27. Moore, C.I., Andermann, M.L.: 2. In: The Vibrissa Resonance Hypothesis, pp. 21–60. CRC Press, Boca Raton (2005)

    Google Scholar 

  28. Neimark, M.A., Andermann, M.L., Hopfield, J.J., Moore, C.I.: Vibrissa resonance as a transduction mechanism for tactile encoding. The Journal of Neuroscience (2003)

    Google Scholar 

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N’Guyen, S., Pirim, P., Meyer, JA. (2011). Texture Discrimination with Artificial Whiskers in the Robot-Rat Psikharpax. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2010. Communications in Computer and Information Science, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18472-7_20

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  • DOI: https://doi.org/10.1007/978-3-642-18472-7_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18471-0

  • Online ISBN: 978-3-642-18472-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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