Advertisement

Autonomous Robots

, Volume 20, Issue 3, pp 277–287 | Cite as

Particle swarm-based olfactory guided search

  • Lino Marques
  • Urbano Nunes
  • A. T. de Almeida
Article

Abstract

This article presents a new algorithm for searching odour sources across large search spaces with groups of mobile robots. The proposed algorithm is inspired in the particle swarm optimization (PSO) method. In this method, the search space is sampled by dynamic particles that use their knowledge about the previous sampled space and share this knowledge with other neighbour searching particles allowing the emergence of efficient local searching behaviours. In this case, chemical searching cues about the potential existence of upwind odour sources are exchanged. By default, the agents tend to avoid each other, leading to the emergence of exploration behaviours when no chemical cue exists in the neighbourhood. This behaviour improves the global searching performance.

The article explains the relevance of searching odour sources with autonomous agents and identifies the main difficulties for solving this problem. A major difficulty is related with the chaotic nature of the odour transport in the atmosphere due to turbulent phenomena. The characteristics of this problem are described in detail and a simulation framework for testing and analysing different odour searching algorithms was constructed. The proposed PSO-based searching algorithm and modified versions of gradient-based searching and biased random walk-based searching strategies were tested in different environmental conditions and the results, showing the effectiveness of the proposed strategy, were analysed and discussed.

Keywords

Olfactive search Cooperative robotics Particle swarm optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Almeida, N., Marques, L., and de Almeida, A. 2003. Fast identification of gas mixtures through the processing of transient responses of an electronic nose. In Proc. of EuroSensors.Google Scholar
  2. Almeida, N., Marques, L., and de Almeida, A. 2004. Directional electronic nose setup—results on the detection of static odour sources. In IEEE Int. Conf. on Sensors.Google Scholar
  3. Anderson, C., Mole, N., Nadarajah, S., and Rydén, T. 2001. Modelling the occurrence of large concentration values in pollutant plumes. In Proc. Int. Cong. on Modelling and Simulation, pp. 911–916.Google Scholar
  4. Balkovsky, E. and Shraiman, B. 2002. Olfactory search at high Reynolds number. Proc National Academy of Science USA, 99(20):12589–12593.Google Scholar
  5. Bénichou, O., Coppey, M., Moreau, M., Suet, P.H., and Voituriez, R. 2005. A stochastic model for intermittent search strategies. Journal of Physics: Condensed Matter, 17(49):S4275–S4286.Google Scholar
  6. Burgard, W., Moors, M., Stachniss, C., and Schneider, F. 2005. coordinated multi- robot exploration. IEEE Transactions on Robotics, 21(3):376–386.Google Scholar
  7. Farrell, J.A., Pang, S., and Li, W. 2003. Plume mapping via hidden markov methods. IEEE Trans. on Systems, Man, and Cybernetics - Part B, 33:850–863.Google Scholar
  8. Furton, K. and Myers, L. 2001. The scientific foundation and efficacy of the use of canines as chemical detectors for explosives. Talanta, 54(3):487–500.Google Scholar
  9. Gage, D. 1993. Randomized Search Strategies with Imperfect Sensors. In Proc. SPIE Conf. on Mobile Robots VIII: 270–279.Google Scholar
  10. Hayes, A.T. 2002. Self-organized robotic system design and autonomous odor localization. Ph.D. thesis, California Institute of Technology.Google Scholar
  11. Hepper, P. and Wells, D. 2005. How many footsteps do dogs need to determine the direction of an odour trail? Chemical Senses, 30(4):291–298.Google Scholar
  12. Holland, O. and Melhuish, C. 1996. Some Adaptive movements of animats with single symmetrical sensors. In Proc. 4th Conf. on Simulation and Adaptive Behavior—From Animals to Animats, 4. pp. 55–64.Google Scholar
  13. Ishida, H., Suetsugu, K., Nakamoto, T., and Moriizumi, T. 1994. Study of autonomous mobile sensing system for localization of odor source using gas sensors and anemometric sensors. Sensors and Actuators, A45:153–157.Google Scholar
  14. Justus, K., Murlis, J., Jones, C., and Cardé, R. 2002. Measurement of odor-plume structure in a wind tunnel using a photoionization detector and a tracer gas. Environ. Fluid Mech, 2:115–142.Google Scholar
  15. Kennedy, J. and Eberhart, R.C. 1995. Particle swarm optimization. In IEEE Int. Conf. on Neural Networks, pp. 1942–1948.Google Scholar
  16. Koopman, B. 1980. Search and Screening: General Principles with Historical Applications. Pergamon Press.Google Scholar
  17. Latombe, J. 1991. Robot Motion Planning, Kluwer.Google Scholar
  18. Marques, L., Almeida, N., and de Almeida, A. 2003a. Mobile robot olfactory sensory system. In Proc. of EuroSensors.Google Scholar
  19. Marques, L., Almeida, N., and de Almeida, A. 2003b. Olfactory sensory system for odour-plume tracking and localization. In IEEE Int. Conf. on Sensors.Google Scholar
  20. Marques, L., Nunes, U., and de Almeida, A. 2002a. Cooperative odour field exploration with genetic algorithms. In Proc. 5th Portuguese Conf. on Automatic Control (CONTROLO 2002), pp. 138–143.Google Scholar
  21. Marques, L., Nunes, U., and de Almeida, A. 2002b. Olfaction-based mobile robot navigation. Thin Solid Films, 418(1):51–58.Google Scholar
  22. Marques, L., Nunes, U., and de Almeida, A. 2003c. Odour searching with autonomous mobile robots: An evolutionary-based approach. In Proc. IEEE Int. Conf. on Advanced Robotics, pp. 494–500.Google Scholar
  23. Müeller, S., Marchetto, J., Airaghi, S., and Koumoutsakos, P. 2002. Optimization based on bacterial chemotaxis. IEEE Trans. on Evolutionary Computation, 6(1):16–29.Google Scholar
  24. Mole, N. and Jones, C. 1994. Concentration fluctuation data from dispersion experiments carried out in stable and unstable conditions. Boundary-Layer Meteorol, 67:41–74.Google Scholar
  25. Mylne, K.R. and Mason, P.J. 1991. Concentration fluctuation measurements in a dispersing plume at a range of up to 1000 m. Quart Journal Royal Meteorological Soc, 117:177–206.Google Scholar
  26. Nielsen, M., Chatwin, P., Jørgensen, H.E., Mole, N., Munro, R., and Ott, S. 2002. Concentration fluctuations in gas releases by industrial accidents - Final report. Technical Report R-1329(EN), Risø Nat. Lab., Denmark.Google Scholar
  27. Parsopoulos, K. and Vrahatis, M. 2002. Recent approaches to global optimization problems through particle swarm optimization. Natural Computing, 1(2–3):235–306.Google Scholar
  28. Passino, K. 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22(3):52–67.Google Scholar
  29. Russell, R., Thiel, D., Deveza, R., and Mackay-Sim, A. 1995. A robotic system to locate hazardous chemical leaks. In Proc. IEEE Int. Conf. on Robotics and Automation, pp. 556–561.Google Scholar
  30. Rutkowski, A., Edwards, S., Willis, M., and Quinn, R. 2004. A robotic platform for testing moth-inspired plume tracking strategies. In Proc. IEEE Int. Conf. on Robotics and Automation, pp. 3319–3324.Google Scholar
  31. Stix, G. 2005. Better than a dog: The search is on for a sensor that bests a canine at detecting explosives. Scientific American Mag, 293(4):74–77.Google Scholar
  32. Stone, J. 1989. Theory of Optimal Search. Academic Press, 2nd edn.Google Scholar
  33. Yang, C. 2005. The state of surveillance. Business Week, pp. 52-59.Google Scholar
  34. Yee, E., Chan, R., Kosteniuk, P., Chandler, G., Biltoft, C., and Bowers, J. 1994. Experimental measurements of concentration and scales in a dispersing plume in the atmospheric surface layer obtained using a very fast response concentration detector. Journal App. Meteorology, 33(8):996–1016.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Lino Marques
    • 1
  • Urbano Nunes
    • 1
  • A. T. de Almeida
    • 1
  1. 1.Institute of Systems and Robotics, Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal

Personalised recommendations