A Graph-Based Formation Algorithm for Odor Plume Tracing

  • Jorge M. Soares
  • A. Pedro Aguiar
  • António M. Pascoal
  • Alcherio Martinoli
Conference paper
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 112)


Odor plume tracing is a challenging robotics application, made difficult by the combination of the patchy characteristics of odor distribution and the slow response of the available sensors. This work proposes a graph-based formation control algorithm to coordinate a group of small robots equipped with odor sensors, with the goal of tracing an odor plume to its source. This approach makes it possible to organize the robots in arbitrary and evolving formation shapes with the aim of improving tracing performance. The algorithm was evaluated in a high-fidelity submicroscopic simulator, using different formations and achieving quick convergence and negligible distance overhead in laminar wind flows.


Odor source localization Plume tracing Formation control Robotic olfaction 



This work was partially funded by project PEst-OE/EEI/LA0009/2013 and grant SFRH/BD/51073/2010 from Fundação para a Ciência e Tecnologia. We sincerely thank Ali Marjovi at DISAL for the detailed and constructive comments.


  1. 1.
    Cabrita, G., Marques, L., Gazi, V.: Virtual cancelation plume for multiple odor source localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5552–5558 (2013). doi: 10.1109/IROS.2013.6697161
  2. 2.
    Cao, M.L., Meng, Q.H., Wang, X.W., Luo, B., Zeng, M., Li, W.: Localization of multiple odor sources via selective olfaction and adapted ant colony optimization algorithm. In: IEEE International Conference on Robotics and Biomimetics, pp. 1222–1227 (2013). doi: 10.1109/ROBIO.2013.6739631
  3. 3.
    de Croon, G., O’Connor, L., Nicol, C., Izzo, D.: Evolutionary robotics approach to odor source localization. Neurocomputing 121, 481–497 (2013). doi: 10.1016/j.neucom.2013.05.028 CrossRefGoogle Scholar
  4. 4.
    Dhariwal, A., Sukhatme, G., Requicha, A.: Bacterium-inspired robots for environmental monitoring. In: IEEE International Conference on Robotics and Automation, pp. 1436–1443 (2004). doi: 10.1109/ROBOT.2004.1308026
  5. 5.
    Distante, C., Indiveri, G., Reina, G.: An application of mobile robotics for olfactory monitoring of hazardous industrial sites. Ind. Rob. Int. J. 36(1), 51–59 (2009). doi: 10.1108/01439910910924675 CrossRefGoogle Scholar
  6. 6.
    Falconi, R., Gowal, S., Martinoli, A.: Graph based distributed control of non-holonomic vehicles endowed with local positioning information engaged in escorting missions. In: IEEE International Conference on Robotics and Automation, pp. 3207–3214 (2010). doi: 10.1109/ROBOT.2010.5509139
  7. 7.
    Farrell, J.A., Murlis, J., Long, X., Li, W., Cardé, R.T.: Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes. Environ. Fluid Mech. 2(1–2), 143–169 (2002). doi: 10.1023/A:1016283702837 CrossRefGoogle Scholar
  8. 8.
    Genovese, V., Dario, P., Magni, R., Odetti, L.: Self organizing behavior and swarm intelligence in a pack of mobile miniature robots in search of pollutants. IEEE/RSJ Int. Conf. Intell. Rob. Syst. 3, 1575–1582 (1992). doi: 10.1109/IROS.1992.594225 CrossRefGoogle Scholar
  9. 9.
    Hartman, J.: A possible method for the rapid estimation of flavours in vegetables. Proc. Am. Soc. Hort. Sci. 64, 335–342 (1954)Google Scholar
  10. 10.
    Hayes, A., Martinoli, A., Goodman, R.: Distributed odor source localization. IEEE Sens. J. 2(3), 260–271 (2002). doi: 10.1109/JSEN.2002.800682 CrossRefGoogle Scholar
  11. 11.
    Ishida, H., Nakamoto, T., Moriizumi, T., Kikas, T., Janata, J.: Plume-tracking robots: a new application of chemical sensors. Biol. Bull. 200(2), 222–226 (2001)CrossRefGoogle Scholar
  12. 12.
    Jatmiko, W., Sekiyama, K., Fukuda, T.: A PSO-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement. IEEE Comput. Intell. Mag. 2(2), 37–51 (2007). doi: 10.1109/MCI.2007.353419 CrossRefGoogle Scholar
  13. 13.
    Khalili, A., Rastegarnia, A., Islam, M.K., Yang, Z.: A bio-inspired cooperative algorithm for distributed source localization with mobile nodes. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3515–3518 (2013). doi: 10.1109/EMBC.2013.6610300
  14. 14.
    Kowadlo, G., Russell, R.A.: Robot odor localization: a taxonomy and survey. Int. J. Robot. Res. 27(8), 869–894 (2008). doi: 10.1177/0278364908095118 CrossRefGoogle Scholar
  15. 15.
    Li, J.G., Meng, Q.H., Wang, Y., Zeng, M.: Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm. Auton. Rob. 30(3), 281–292 (2011). doi: 10.1007/s10514-011-9219-2 CrossRefGoogle Scholar
  16. 16.
    Lilienthal, A., Duckett, T.: Experimental analysis of gas-sensitive Braitenberg vehicles. Adv. Robot. 18(8), 817–834 (2004). doi: 10.1163/1568553041738103 CrossRefGoogle Scholar
  17. 17.
    Lochmatter, T.: Bio-inspired and probabilistic algorithms for distributed odor source localization using mobile robots. Ph.D. thesis 4628, EPFL (2010). doi: 10.5075/epfl-thesis-4628
  18. 18.
    Lochmatter, T., Göl, E., Navarro, I., Martinoli, A.: A plume tracking algorithm based on crosswind formations. In: International Symposium on Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics (2013), vol. 83, pp. 91–102 (2010). doi: 10.1007/978-3-642-32723-0_7
  19. 19.
    Marjovi, A., Marques, L.: Optimal swarm formation for odor plume finding. IEEE Trans. Cybern. 99 (2014). doi: 10.1109/TCYB.2014.2306291
  20. 20.
    Marques, L., Nunes, U., de Almeida, A.T.: Olfaction-based mobile robot navigation. Thin Solid Films 418(1), 51–58 (2002). doi: 10.1016/S0040-6090(02)00593-X CrossRefGoogle Scholar
  21. 21.
    Marques, L., Nunes, U., Almeida, A.T.: Particle swarm-based olfactory guided search. Auton. Rob. 20(3), 277–287 (2006). doi: 10.1007/s10514-006-7567-0 CrossRefGoogle Scholar
  22. 22.
    Mesbahi, M., Egerstedt, M.: Graph Theoretic Methods in Multiagent Networks. Princeton University Press, Princeton (2010)Google Scholar
  23. 23.
    Michel, O.: Webots: professional mobile robot simulation. Int. J. Adv. Rob. Syst. 1(1), 39–42 (2004). doi: 10.5772/5618 Google Scholar
  24. 24.
    Moncrieff, R.W.: An instrument for measuring and classifying odors. J. Appl. Physiol. 16(4), 742–749 (1961)Google Scholar
  25. 25.
    Pugh, J., Raemy, X., Favre, C., Falconi, R., Martinoli, A.: A Fast onboard relative positioning module for multirobot systems. IEEE/ASME Trans. Mechatron. 14(2), 151–162 (2009). doi: 10.1109/TMECH.2008.2011810 CrossRefGoogle Scholar
  26. 26.
    Roberts, P.J.W., Webster, D.R.: Turbulent Diffusion. ASCE Press, Reston, Virginia (2002)Google Scholar
  27. 27.
    Rozas, R., Morales, J., Vega, D.: Artificial smell detection for robotic navigation. In: International Conference on Advanced Robotics, pp. 1730–1733 (1991). doi: 10.1109/ICAR.1991.240354
  28. 28.
    Vergassola, M., Villermaux, E., Shraiman, B.I.: ‘Infotaxis’ as a strategy for searching without gradients. Nature 445(7126), 406–409 (2007). doi: 10.1038/nature05464 CrossRefGoogle Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  • Jorge M. Soares
    • 1
    • 2
  • A. Pedro Aguiar
    • 3
  • António M. Pascoal
    • 2
    • 4
  • Alcherio Martinoli
    • 1
  1. 1.Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental EngineeringÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Institute for Systems and Robotics, Instituto Superior TécnicoUniversity of LisbonLisbonPortugal
  3. 3.Department of Electrical and Computer Engineering, Faculty of EngineeringUniversity of PortoPortoPortugal
  4. 4.National Institute of OceanographyDona PaulaGoaIndia

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