Estimation of Gaussian Plume Model Parameters Using the Simulated Annealing Algorithm

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 253)

Abstract

This article presents a novel cost function for estimating the parameters of the Gaussian plume model using simulated annealing. The novel cost function takes into account the meandering and intermittency phenomena found on dispersing plumes. The proposed method was validated using real gas sensor data sampled by a swarm of 5 robots performing the Decentralized Asynchronous Particle Swarm Optimization for plume tracing under a controlled environment.

Keywords

Gaussian plume model simulated annealing odor plume estimation swarm robotics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ishida, H., Wada, Y., Matsukura, H.: Chemical sensing in robotic applications: A review (2012)Google Scholar
  2. 2.
    Lochmatter, T., Aydın Göl, E., Navarro, I., Martinoli, A.: A plume tracking algorithm based on crosswind formations. In: Martinoli, A., Mondada, F., Correll, N., Mermoud, G., Egerstedt, M., Hsieh, M.A., Parker, L.E., Støy, K. (eds.) Distributed Autonomous Robotic Systems. STAR, vol. 83, pp. 91–102. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Neumann, P.P., Hernandez Bennetts, V., Lilienthal, A.J., Bartholmai, M., Schiller, J.H.: Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms. Advanced Robotics 27(9), 725–738 (2013)CrossRefGoogle Scholar
  4. 4.
    Marques, L., Nunes, U., de Almeida, A.: Olfaction-based mobile robot navigation. Thin Solid Films 418(1), 51–58 (2002); 1st Int. School on Gas Sensors Google Scholar
  5. 5.
    Cabrita, G., Marques, L., Gazi, V.: Virtual Cancelation Plume for Multiple Odor Source Localization. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2013 (2013)Google Scholar
  6. 6.
    Lehning, M., Shonnard, D.R., Chang, D.P., Bell, R.L.: An inversion algorithm for determining area-source emissions from downwind concentration measurements. Air & Waste 44(10), 1204–1213 (1994)Google Scholar
  7. 7.
    Flesch, T.K., Wilson, J.D., Harper, L.A., Crenna, B.P.: Estimating gas emissions from a farm with an inverse-dispersion technique. Atmospheric Environment 39(27), 4863–4874 (2005)CrossRefGoogle Scholar
  8. 8.
    Thomson, L.C., Hirst, B., Gibson, G., Gillespie, S., Jonathan, P., Skeldon, K.D., Padgett, M.J.: An improved algorithm for locating a gas source using inverse methods. Atmospheric Environment 41(6), 1128–1134 (2007)CrossRefGoogle Scholar
  9. 9.
    Euler, J., Stryk, O.V.: Optimal Cooperative Control of Mobile Sensors for Dynamic Process Estimation. In: RSS213 Workshop on Robotics for Environmental Monitoring (2013)Google Scholar
  10. 10.
    Arya, S.P.: Air Pollution Meteorology and Dispersion. Oxford University Press (1999)Google Scholar
  11. 11.
    Yee, E., Chan, R., Kosteniuk, P., Chandler, G., Biltoft, C., Bowers, J.: The vertical structure of concentration fluctuation statistics in plumes dispersing in the atmospheric surface layer. Boundary-Layer Meteorology 76(1-2), 41–67 (1995)CrossRefGoogle Scholar
  12. 12.
    Murlis, J., Elkinton, J., Card, R.: Odor plumes and how insects use them. Annu. Rev. Entomol. 37, 505–532 (1992)CrossRefGoogle Scholar
  13. 13.
    Yee, E., Chan, R.: A simple model for the probability density function of concentration fluctuations in atmospheric plumes. Atmospheric Environment 31(7), 991–1002 (1997)CrossRefGoogle Scholar
  14. 14.
    Kirkpatrick, S., Gelatt Jr., D., Vecchi, M.P.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Kirkpatrick, S.: Optimization by simulated annealing: Quantitative studies. Journal of statistical physics 34(5-6), 975–986 (1984)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. The Journal of Chemical Physics 21, 1087 (1953)CrossRefGoogle Scholar
  17. 17.
    Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov chain Monte Carlo in practice, vol. 2. CRC Press (1996)Google Scholar
  18. 18.
    Marques, L., Nunes, U., de Almeida, A.: Cooperative odour field exploration with genetic algorithms. In: Proc. 5th Portuguese Conf. on Automatic Control (CONTROLO 2002), pp. 138–143 (2002)Google Scholar
  19. 19.
    Marques, L., Nunes, U., De Almeida, A.: Odour searching with autonomous mobile robots: An evolutionary-based approach. In: Proceedings of the IEEE Int. Conf. on Advanced Robotics, pp. 494–500 (2003)Google Scholar
  20. 20.
    Marques, L., Nunes, U., De Almeida, A.: Particle swarm-based olfactory guided search. Autonomous Robots 20(3), 277–287 (2006)CrossRefGoogle Scholar
  21. 21.
    Turduev, M., Cabrita, G., Kırtay, M., Gazi, V., Marques, L.: Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms. Journal of Autonomous Agents and Multi-Agent Systems (2013)Google Scholar
  22. 22.
    Lochmatter, T., Roduit, P., Cianci, C., Correll, N., Jacot, J., Martinoli, A.: Swistrack-a flexible open source tracking software for multi-agent systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 4004–4010. IEEE (2008)Google Scholar
  23. 23.
    Hawkins, D.M., Cressie, N.: Robust kriging - a proposal. Journal of the International Association for Mathematical Geology 16(1), 3–18 (1984)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal

Personalised recommendations