Aggregation Behaviour as a Source of Collective Decision in a Group of Cockroach-Like-Robots

  • Simon Garnier
  • Christian Jost
  • Raphaël Jeanson
  • Jacques Gautrais
  • Masoud Asadpour
  • Gilles Caprari
  • Guy Theraulaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)

Abstract

In group-living animals, aggregation favours interactions and information exchanges between individuals, and thus allows the emergence of complex collective behaviors. In previous works, a model of a self-enhanced aggregation was deduced from experiments with the cockroach Blattella germanica. In the present work, this model was implemented in micro-robots Alice and successfully reproduced the agregation dynamics observed in a group of cockroaches. We showed that this aggregation process, based on a small set of simple behavioral rules of interaction, can be used by the group of robots to select collectively an aggregation site among two identical or different shelters. Moreover, we showed that the aggregation mechanism allows the robots as a group to “estimate” the size of each shelter during the collective decision-making process, a capacity which is not explicitly coded at the individual level.

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References

  1. 1.
    Agassounon, W., Martinoli, A.: A macroscopic model of an aggregation experiment using embodied agents in groups of time-varying sizes. In: Proceedings of the 2002 IEEE Systems, Man and Cybernetics Conference, Hammamet, Tunisia. IEEE Press, Los Alamitos (2002)Google Scholar
  2. 2.
    Ame, J.-M., Rivault, C., Deneubourg, J.-L.: Cockroach aggregation based on strain odour recognition. Animal Behaviour 68(4), 793–801 (2004)CrossRefGoogle Scholar
  3. 3.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  4. 4.
    Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-organization in biological systems. Princeton University Press, Princeton (2001)Google Scholar
  5. 5.
    Caprari, G., Estier, T., Siegwart, R.: Fascination of down scaling – Alice the sugar cube robot. Journal of Micromechatronics 1(3), 177–189 (2002)CrossRefGoogle Scholar
  6. 6.
    Deneubourg, J.L., Lioni, A., Detrain, C.: Dynamics of aggregation and emergence of cooperation. Biological Bulletin 202(3), 262–267 (2002)CrossRefGoogle Scholar
  7. 7.
    Grassé, P.-P.: La reconstruction du nid et les coordinations inter-individuelles chez Bellicositermes Natalensis et Cubitermes sp. La théorie de la stigmergie: essai d’interprétation du comportement des termites constructeurs. Insectes sociaux 6, 41–81 (1959)CrossRefGoogle Scholar
  8. 8.
    Holland, O., Melhuish, C.: Stigmergy, self-organisation, and sorting in collective robotics. Artificial Life 5, 173–202 (1999)CrossRefGoogle Scholar
  9. 9.
    Jeanson, R., Blanco, S., Fournier, R., Deneubourg, J.L., Fourcassié, V., Theraulaz, G.: A model of animal movements in a bounded space. Journal of Theoretical Biology 225(4), 443–451 (2003)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Jeanson, R., Rivault, C., Deneubourg, J.-L., Blanco, S., Fournier, R., Jost, C., Theraulaz, G.: Self-organized aggregation in cockroaches. Animal Behaviour 69(1), 169–180 (2005)CrossRefGoogle Scholar
  11. 11.
    Ledoux, A.: Étude experimentale du grégarisme et de l’interattraction sociale chez les Blattidés. Annales des Sciences Naturelles Zoologie et Biologie Animale 7, 76–103 (1945)Google Scholar
  12. 12.
    Martinoli, A., Mondada, F.: Collective and cooperative group behaviours: biologically inspired experiments in robotics. In: Khatib, O., Salisbury, J.K. (eds.) Proceedings of the Fourth International Symposium on Experimental Robotics, June 1995, pp. 3–10. LNCIS, Stanford (1995)Google Scholar
  13. 13.
    R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2004) ISBN 3-900051-07-0Google Scholar
  14. 14.
    Rust, M.K., Owens, J.M., Reierson, D.A.: Understanding and controlling the german cockroach. Oxford University Press, Oxford (1995)Google Scholar
  15. 15.
    Şahin, E.: Swarm robotics: From sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Seeley, T.D., Camazine, S., Sneyd, J.: Collective decision-making in honey bees: how colonies choose among nectar sources. Behavioural Ecology and Sociobiology 28, 277–290 (1991)CrossRefGoogle Scholar
  17. 17.
    Sugawara, K., Sano, M.: Cooperative acceleration of task performance: foraging behavior of interacting multi-robots system. Physica D: Nonlinear Phenomena 100(3/4), 343–354 (1997)MATHCrossRefGoogle Scholar
  18. 18.
    Wagner, I.A., Bruckstein, A.M.: Ant robotics. Annals of Mathematics and Artificial Intelligence 31, 1–238 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Simon Garnier
    • 1
  • Christian Jost
    • 1
  • Raphaël Jeanson
    • 1
  • Jacques Gautrais
    • 1
  • Masoud Asadpour
    • 2
  • Gilles Caprari
    • 2
  • Guy Theraulaz
    • 1
  1. 1.Centre de Recherches sur la Cognition Animale, UMR-CNRS 5169Université Paul SabatierToulouse cedex 4France
  2. 2.Autonomous Systems LabSwiss Federal Institute of Technology, (EPFL)LausanneSwitzerland

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