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Integration of Directional Smell Sense on an UGV

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Advances in Artificial Intelligence (MICAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7629))

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Abstract

The olfaction sense in animals has been exploited on some applications like chemicals detection and during search and rescue operations. There has been considerable attention to bring this capability to mobile robots implementing odor source localization techniques. The objective of this research is to use a robot with a smell sensor inspired by nature, to identify the direction from where the odor is coming. The design of the sensor consists in two bio-inspired nostrils, separated by a septum, integrating a full nose system with the ability of inhalation and exhalation, which helps to desaturate the sensors. It has been implemented with a bus architecture based on Controller Area Network (CAN) to make the integration of the sensor relatively fast and without need of interfering with the existing systems of the vehicle. After several experiments, we conclude that the designed sensor can reach its objective even in an outdoor environment.

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References

  1. Shuzi, S.G., Lewis, F.L.: Autonomous Mobile Robots: Sensing, Control, Decision Making and Applications. Taylor & Francis Group, Florida (2006)

    Google Scholar 

  2. del Bosque, J., Hassard, C., Gordillo, J.L.: Velocity Control of an Electric Vehicle over a CAN Network. In: 2011 10th Mexican International Conference on Artificial Intelligence (MICAI), pp. 121–126 (2011)

    Google Scholar 

  3. Martinoli, A., Lochmatter, T., Raemy, X.: Odor source localization with mobile robots. Bulletin of the Swiss Society for Automatic Control 46, 11–14 (2007)

    Google Scholar 

  4. Martinoli, A., Lochmatter, T.: Theoretical analysis of three bio-inspired plume tracking algorithms. In: IEEE International Conference on Robotics and Automation (2009)

    Google Scholar 

  5. Lilienthal, A., Duckett, T.: Experimental analysis of smelling braitenberg vehicles. In: 11th International Conference on Advanced Robotics (2003)

    Google Scholar 

  6. Martinoli, A., Lochmatter, T.: Simulation experiments with bio-inspired algorithms for odor source localization in laminar wind flow. In: 7th International Conference on Machine Learning and Applications (2008)

    Google Scholar 

  7. Wadel, M., Lilienthal, A., Duckett, T., Weimar, U., Zell, A.: Gas distribution in unventilated indoor environments inspected by a mobile robot. In: 11th International Conference on Advanced Robotics (2003)

    Google Scholar 

  8. Loutfi, A., Coradeschi, S., Lilienthal, A., Gonzalez, J.: Gas distribution mapping of multiple odour sources using a mobile robot. Robotica 27, 311–319 (2009)

    Article  Google Scholar 

  9. Lilienthal, A., Duckett, T.: Building gas concentration gridmaps with a mobile robot. Robotics and Autonomous Systems 48, 3–16 (2004)

    Article  Google Scholar 

  10. Kowadlo, G., Russell, R.A.: Naive Physics for Effective Odour Localisation. In: Australian Conference on Robotics and Automation, Brisbane (2003)

    Google Scholar 

  11. Cabrita, G., Sousa, P., Marques, L.: Odor guided exploration and plume tracking: Particle Plume Explorer. In: Proc. of European Conf. on Mobile Robotics (ECMR), Örebro, pp. 165–170 (2011)

    Google Scholar 

  12. Kowadlo, G., Russell, R.A.: Robot odor localization: a taxonomy and survey. Int. J. Rob. Res. 27, 869–894 (2008)

    Article  Google Scholar 

  13. Crank, J.: The mathematics of diffusion. Oxford University Press, New York (1976)

    Google Scholar 

  14. Pearce, T.C., Schiffman, S.S., Nagle, H.T., Gardner, J.W.: Handbook of Machine Olfaction: Electronic Nose Technology. Wiley-VCH, Weinheim (2003)

    Google Scholar 

  15. Doty, R.L.: Handbook of olfaction and gustation. Marcel Dekker, Inc., New York (2001)

    Google Scholar 

  16. Fraden, J.: Handbook of modern sensors physics, designs, and applications. Springer (2003)

    Google Scholar 

  17. Villarreal, B.L., Gordillo, J.L.: Directional Aptitude Analysis in Odor Source Localization Techniques for Rescue Robots Applications. In: 2011 10th Mexican International Conference on Artificial Intelligence (MICAI), pp. 109–114 (2011)

    Google Scholar 

  18. Craven, B.A., Paterson, E.G., Settle, G.S.: The fluid dynamics of canine olfaction:unique nasal airflow patterns as an explanation of macrosmia. J. R. Soc. Interface 7, 933–943 (2009)

    Article  Google Scholar 

  19. Barrett, K.E., Barman, S.M., Boitano, S., Brooks, H.: Ganong’s Review of Medical Physiology. McGraw-Hill, New York (2010)

    Google Scholar 

  20. Gurram, S.K., Conrad, J.M.: Implementation of CAN bus in an autonomous all-terrain vehicle. In: Proceedings of IEEE Southeastcon, pp. 250–254 (2011)

    Google Scholar 

  21. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, New York (2009)

    Google Scholar 

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Lorena Villarreal, B., Hassard, C., Gordillo, J.L. (2013). Integration of Directional Smell Sense on an UGV. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37807-2_24

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  • DOI: https://doi.org/10.1007/978-3-642-37807-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37806-5

  • Online ISBN: 978-3-642-37807-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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