Self-Organised Task Allocation in a Group of Robots

  • Thomas Halva Labella
  • Marco Dorigo
  • Jean-Louis Deneubourg


Robot foraging, a frequently used test application for collective robotics, consists in a group of robots retrieving a set of opportunely defined objects to a target location. A commonly observed experimental result is that the retrieving efficiency of the group of robots, measured for example as the number of units retrieved by a robot in a given time interval, tends to decrease with increasing group sizes. In this paper we describe a biology inspired method for tuning the number of foraging robots in order to improve the group efficiency. As a result of our experiments, in which robots use only locally available information and do not communicate with each other, we observe self-organised task allocation. This task allocation is effective in exploiting mechanical differences among the robots inducing specialisation in the robots activities.


Idleness Time Task Allocation Allocation Mechanism Mechanical Difference Single Robot 
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  1. W. Agassounon and A. Martinoli. Efficiency and robustness of thresholdbased distributed allocation algorithms in multi-agent systems. In C. Castelfranchi and W.L. Johnson, editors, Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-02), pages 1090–1097. ACM Press, New York, NY, USA, 2002.CrossRefGoogle Scholar
  2. T. Balch. The impact of diversity on performance in multi-robot foraging. In O. Etzioni, J.P. Müller, and J.M. Bradshaw, editors, Proceedings of the Third International Conference on Autonomous Agents (Agents’99), pages 92–99. ACM Press, New York, NY, USA, 1999.CrossRefGoogle Scholar
  3. T. Balch and R.C. Arkin. Communication in reactive multiagent robotic systems. Autonomous Robots, 1(1):27–52, 1994.CrossRefGoogle Scholar
  4. E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York, USA, 1999.0MATHGoogle Scholar
  5. E. Bonabeau, G. Theraulaz, and J.-L. Deneubourg. Quantitative study of the fixed threshold model for the regulation of division of labor in insect societies. Proceedings of the Royal Society of London, Series B-Biological Sciences, 263:1565–1569, 1996.CrossRefGoogle Scholar
  6. S. Camazine, J.-L. Deneubourg, N.R. Pranks, J. Sneyd, G. Theraulaz, and E. Bonabeau. Self-Organisation in Biological Systems. Princeton University Press, Princeton, NJ, USA, 2001.Google Scholar
  7. Y.U. Cao, A.S. Fukunaga, and A.B. Kahng. Cooperative mobile robotics: Antecedents and directions. Autonomous Robots, 4(1):7–27, 1997.CrossRefGoogle Scholar
  8. J.-L. Deneubourg, S. Goss, J.M. Pasteels, D. Fresneau, and J.-P. Lachaud. Self-organization mechanisms in ant societies (II): Learning in foraging and division of labor. In J.M. Pasteels and J.-L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, volume 54 of Experientia Supplementum, pages 177–196. Birkhäuser Verlag, Basel, Switzerland, 1987.Google Scholar
  9. M. Dorigo, E. Bonabeau, and G. Theraulaz. Ant algorithms and stigmergy. Future Generation Computer Systems, 16(8):851–871, 2000.CrossRefGoogle Scholar
  10. B.P. Gerkey and M.J. Matarić. A framework for studying multi-robot task allocation. In A.C. Schultz, L.E. Parker, and F.E. Schneider, editors, Multi-Robot Systems, pages 15–26. Kluwer Academic Publishers, Dordrecht, The Netherlands, 2003.Google Scholar
  11. D. Goldberg and M.J. Matarić. Interference as a tool for designing and evaluating multi-robot controllers. In Proceedings of the 14th National Conference on Artificial Intelligence (AAAI-97), pages 637–642. MIT Press, Cambridge, MA, USA, 1997.Google Scholar
  12. P. P. Grassé. La reconstruction du nid et les coordinations inter-individuelles chez Bellicositermes natalensis et Cubitermes. La théorie de la stigmergie: essai d’interpretation des termites constructeurs. Insectes Sociaux, 6:41–83, 1959.CrossRefGoogle Scholar
  13. M.J.B. Krieger and J.-B. Billeter. The call of duty: Self-organised task allocation in a population of up to twelve mobile robots. Robotics and Autonomous Systems, 30(1–2):65–84, 2000.CrossRefGoogle Scholar
  14. T.H. Labella, M. Dorigo, and J.-L. Deneubourg. Efficiency and task allocation in prey retrieval. In A.J. Ijspeert, D. Mange, M. Murata, and S. Nishio, editors, Proceedings of the First International Workshop on Biologically Inspired Approaches to Advanced Information Technology (Bio-ADIT2004), Lecture Notes in Computer Science, pages 32–47. Springer Verlag, Heidelberg, Germany, 2004.Google Scholar
  15. M. Schneider-Fontán and M.J. Matarić. A study of territoriality: The role of critical mass in adaptive task division. In P. Maes, M.J. Matarić, J.-A. Meyer, J. Pollack, and S.W. Wilson, editors, From Animals to Animats 4, Fourth International Conference on Simulation of Adaptive Behavior (SAB-96), pages 553–561. MIT Press/Bradford Books, Cambridge, MA, USA, 1996.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Thomas Halva Labella
    • 1
  • Marco Dorigo
    • 1
  • Jean-Louis Deneubourg
    • 1
  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium

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