Ergodic Dynamics for Large-Scale Distributed Robot Systems

  • Dylan A. Shell
  • Maja J. Matarić
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4135)


Intelligent autonomous robotics is a promising area with many potential applications that could benefit from non-traditional models of computation. Information processing systems interfaced with the real world must deal with a continuous and uncertain environment, and must cope with interactions across a range of time-scales. Robotics problems resist existing tools and, consequently, new perspectives are needed to address these challenges. Toward that end, we describe a dynamics-based model for computing in large-scale distributed robot systems. The proposed method employs a compositional approach, constructing robot controllers from ergodic processes. We describe application of the method to two multi-robot tasks: decentralised task allocation, and collective strategy selection.


Obstacle Avoidance Task Allocation Robot System Home Region Homogeneous Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adamatzky, A., De Lacy Costello, B., Melhuish, C., Ratcliffe, N.: Experimental reaction–diffusion chemical processors for robot path planning. Journal of Intelligent Robotic Systems 37, 233–249 (2003)CrossRefMATHGoogle Scholar
  2. 2.
    Arkin, R.C., Balch, T., Nitz, E.: Communication of behavioral state in multi-agent retrieval tasks. In: Proceedings IEEE Conference on Robotics and Automation, Atlanta, GA, pp. 588–594 (1993)Google Scholar
  3. 3.
    Beckers, R., Holland, O.E., Deneubourg, J.-L.: From Local Actions to Global Tasks: Stigmergy and Collective Robotics. In: Artificial Life IV, Cambridge, MA, pp. 181–189 (July 1994)Google Scholar
  4. 4.
    Bekey, G.A.: Autonomous Robots: From Biological Inspiration to Implementation and Control. MIT Press, Cambridge (2005)Google Scholar
  5. 5.
    Berlekamp, E.R., Conway, J.H., Guy, R.K.: Winning Ways for Your Mathematical Plays. Academic Press, New York (1982)MATHGoogle Scholar
  6. 6.
    Bonabeau, E., Dorigo, M., Thraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)MATHGoogle Scholar
  7. 7.
    Brooks, R.A.: Intelligence Without Reason. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI 1991), Sydney, Australia, pp. 569–595 (1991)Google Scholar
  8. 8.
    Brooks, R.A.: Cambrian Intelligence: The Early History of the New AI. MIT Press, Cambridge (2001)Google Scholar
  9. 9.
    Deneubourg, J.-L., Goss, S., Franks, N.R., Sendova-Franks, A.B., Detrain, C., Chrétien, L.: The Dynamics of Collective Sorting Robot-like Ants and Ant-like Robots. In: Proc. First International Conference on Simulation of Adaptive Behavior, Paris, France, pp. 356–363 (1990)Google Scholar
  10. 10.
    Donald, B., Jennings, J., Rus, D.: Minimalism + Distribution = Supermodularity. Journal of Experimental and Theoretical AI 9(20), 293–321 (1997)CrossRefGoogle Scholar
  11. 11.
    Fisher, M.E.: The theory of equilibrium critical phenomena. Reports on Progress in Physics 30, 615–730 (1967)CrossRefGoogle Scholar
  12. 12.
    Goldberg, D.: Evaluating the Dynamics of Agent-Environment Interaction, Department of Computer Science, University of Southern California (May, 2001)Google Scholar
  13. 13.
    Hogg, T., Huberman, B.A.: Controlling chaos in distributed systems. IEEE Transactions on Systems, Man, and Cybernetics (Special Section on DAI) 21(6), 1325–1332 (1991)CrossRefGoogle Scholar
  14. 14.
    Holland, O.E., Melhuish, C.: Stigmergy, Self-Organization, and Sorting in Collective Robotics. Artificial Life 5(2), 173–202 (1999)CrossRefGoogle Scholar
  15. 15.
    Jones, C.V., Matarić, M.J.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, pp. 1969–1974 (October 2003)Google Scholar
  16. 16.
    Jones, C.V., Shell, D.A., Matarić, M.J., Gerkey, B.P.: Principled Approaches to the Design of Multi-Robot Systems. In: Invited contribution to Workshop on Networked Robotics, International Conference on Intelligent Robots and Systems, Sendai, Japan, pp. 71–80 (2004)Google Scholar
  17. 17.
    Kube, C.R., Zhang, H.: Collective robotics: From social insects to robots. Adaptive Behavior 2(2), 189–219 (1993)CrossRefGoogle Scholar
  18. 18.
    Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Norwell (1991)Google Scholar
  19. 19.
    Østergaard, E., Sukhatme, G.S., Matarić, M.J.: Emergent Bucket Brigading — A Simple Mechanism for Improving Performance in Multi-Robot Constrained-Space Foraging Tasks. In: International Conference on Autonomous Agents, Montreal, Canada, pp. 29–30 (May 2001)Google Scholar
  20. 20.
    Parker, L.E.: ALLIANCE: An architecture for fault-tolerant multi-robot cooperation. IEEE Transactions on Robotics and Automation 14(2), 220–240 (1998)CrossRefGoogle Scholar
  21. 21.
    Payton, D., Daily, M., Estkowski, R., Howard, M., Lee, C.: Pheromone robotics. Autonomous Robots 11(3), 319–324 (2001)CrossRefMATHGoogle Scholar
  22. 22.
    Pomerleau, D.: Neural Network Perception for Mobile Robot Guidance. Kluwer Academic Publishers, Norwell (1993)Google Scholar
  23. 23.
    Simon, H.A.: The Sciences of the Artificial, 3rd edn. MIT Press, Cambridge (1996)Google Scholar
  24. 24.
    Small, J.S.: The Analogue Alternative: The Electric Analogue Computer in Britain and the USA, pp. 1930–1975. Routledge, London and New York (2001)Google Scholar
  25. 25.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)MATHGoogle Scholar
  26. 26.
    Vaughan, R.T., Støy, K., Sukhatme, G.S., Matarić, M.J.: Blazing a trail: insect-inspired resource transportation by a robot team. In: Proceedings of the International Symposium on Distributed Autonomous Robotic Systems, Knoxville, TN, pp. 111–120 (2000)Google Scholar
  27. 27.
    Vergis, A., Steiglitz, K., Dickinson, B.: The complexity of analog computation. Mathematics and Computers in Simulation 28, 91–113 (1986)CrossRefMATHGoogle Scholar
  28. 28.
    Weiser, M.: Some Computer Science issues in Ubiquitous Computing. Communications of the ACM 36(7), 75–84 (1993)CrossRefGoogle Scholar
  29. 29.
    Wiener, N.: Cybernetics, or Control and Communication in the Animal and the Machine, 2nd edn. MIT Press, Cambridge (1962)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dylan A. Shell
    • 1
  • Maja J. Matarić
    • 1
  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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