Autonomous Robots

, Volume 3, Issue 4, pp 375–397 | Cite as

A taxonomy for multi-agent robotics

  • Gregory Dudek
  • Michael R. M. Jenkin
  • Evangelos Milios
  • David Wilkes


A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multi-agent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.


mobile robotics robotic collectives 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aguilar, L., Alami, R., Fleury, S., Herrb, M., Ingrand, F., and Robert, F. 1995. Ten autonomous mobile robots (and even more) in a route network like environment. In Proc. IEEE/RSJ IROS, 2:260–267, Pittsburgh, PA.Google Scholar
  2. Anderson, M.O., McKay, M.D., and Richardson, B.S. 1996. Multirobot automated indoor floor characterization team. In Proc. IEEE R&A, Minneapolis, Minnesota, pp. 1750–1753.Google Scholar
  3. Anderson, T.L. and Donath, M. 1991. Animal behavior as a paradigm for developing robot autonomy. In Designing Autonomous Agents, MIT Press, pp. 145–168.Google Scholar
  4. Arkin, R., Balch, T., and Nitz, E. 1993. Communication of behavioral state in multi-agent retrieval tasks. In Proc. IEEE R&A, 3:588–594, Atlanta, GA.Google Scholar
  5. Arkin, R.C. and Hobbs, J.D. 1992. Dimensions of communication and social organization in multi-agent robotics systems. In Proc. SAB 92.Google Scholar
  6. Balch, T. and Arkin, R.C. 1994. Communication in reactive multiagent robotic systems. Autonomous Robots, 1(1):27–52.Google Scholar
  7. Balch, T. and Arkin, R.C. 1995. Motor schema-based formation control for multiagent robot teams. In 1995 International Conference on Multiagent Systems, San Francisco, CA, pp. 10–16.Google Scholar
  8. Beni, G. and Wang, J. 1989. Swarm intelligence in cellular robotic systems. In Proc. NATO Advanced Workshop on Robotics and Biological Systems, Il Ciocco, Tuscany, Italy.Google Scholar
  9. Boas, P. 1989. Machine models and simulations. echnical Report CT-89-02, Institute for language, logic and information, University of Amsterdam.Google Scholar
  10. Brooks, R.A. 1986. A robust layered control system for a mobile robot. IEEE J. of Robotics and Automation, 2:14–23.Google Scholar
  11. Brooks, R.A. 1991. Intelligence without reason. Technical Report AI Memo No. 1293, MIT.Google Scholar
  12. Brown, R. and Jennkings, J. 1995. A pusher/steerer model for strongly cooperative mobile robot manipulation. In Proc. IEEE/ RSJ IROS, Pittsburgh, PA, 3:562–568.Google Scholar
  13. Cao, Y.U., Fukunaga, A.S., Kahng, A.B., and Meng, F. 1995. Cooperative mobile robotics: Antecedents and directions. In Proc. IEEE/RSJ IROS, Pittsburgh, PA, 1:226–234.Google Scholar
  14. Causse, O. and Pampagnin, L.H. 1995. Management of a multirobot system in a public environment. In Proc. IEEE/RSJ IROS, Pittsburgh, PA, 2:246–252.Google Scholar
  15. Dennis, J. and Schnabel, R. 1983. Numerical Methods for Uncon-strained Optimization and Nonlinear Equations. Prentice Hall, Englewood Cliffs, NJ.Google Scholar
  16. Dickmanns, E. and Zapp, A. 1987. Autonomous high speed road vehicle guidance by computer vision. In Proc. 10th World Congree on Automatic Control, Munich, Germany.Google Scholar
  17. Dickson, W., Cannon, R., and Rock, S. 1996. Symbolic dynamic modelling and analysis of object/robot-team systems with experiments. In Proc. IEEE R&A, Minneapolis, Minnesota, pp. 1413–1420.Google Scholar
  18. Dudek, G., Jenkin, M., and David Wilkes, E.M. 1989. Using multiple markers in graph exploration. In Proc. Symposium on Advances in Intelligent Robotics Systems: Conference on Mobile Robotics, Philadelphia, PA. International Society for Optical Engineering.Google Scholar
  19. Dudek, G., Jenkin, M., Milios, E., and Wilkes, D. 1991. Robotic exploration as graph construction. IEEE Trans. on Robotics and Automation, 7(6):859–864.Google Scholar
  20. Dudek, G., Jenkin, M., Milios, E., and Wilkes, D. 1993a. Organizational characteristics for multi-agent robotic systems. In Proc. Vision Interface '93, North York, Canada, pp. 91–96.Google Scholar
  21. Dudek, G., Jenkin, M., Milios, E., and Wilkes, D. 1993b. Robust positioning with a multi-agent robotic system. In Proc. IJCAI-93 Workshop on Dynamically Interacting Robots, pp. 118–123.Google Scholar
  22. Dudek, G., Jenkin, M., Milios, E., and Wilkes, D. 1993c. A taxonomy for swarm robotics. In Proc. IEEE/RSJ IROS, Yokohama, Japan, pp. 441–447.Google Scholar
  23. Dudek, G., Jenkin, M., Milios, E., and Wilkes, D. 1995. Experiments in sensing and communication for robot convoy navigation. In Proc. IEEE/RSJ IROS, Pittsburgh, PA, 2:268–273.Google Scholar
  24. Fich, F.E., Radge, P.L., and Widgerson, A. 1988. Relations between concurrent-write models of parallel computation. SIAM J. Comput., 17:606–627.Google Scholar
  25. Fletcher, R. 1987. Practical Methods of Optimization, 2nd Edition. John Wiley: New York, NY.Google Scholar
  26. Gage, D.W. 1992. Command control for many-robot systems. InProc. AUVS-92, Technical Symposium of the Association for Unmanned Vehicle Systems, Huntsville, AL, pp. 22–24.Google Scholar
  27. Habib, M.K., Asama, H., Ishida, Y., Matsumoto, A., and Endo, I. 1992. Simulation environment for an autonomous decentralized multi-agent robotic system. In Proc. IEEE/RSJ IROS, Raleigh, NC, pp. 1550–1557.Google Scholar
  28. Hackwood, S. and Beni, G. 1992. Self-organization of sensors for swarm intelligence. In Proc. IEEE R&A, pp. 819–829.Google Scholar
  29. Hertz, J., Krogh, A., and Palmer, R. 1991. Introduction to the Theory of Neural Computing, Adison-Wesley: Redwood City, CA.Google Scholar
  30. Hopcroft, J. and Ullman, J. 1979. Introduction to Automata Theory, Languages and Computation. Addison-Wesley: Reading, MA.Google Scholar
  31. Kurabayashi, D., Ota, J., Arai, T., and Yoshida, E. 1996. Cooperative sweeping by multiple mobile robots. In Proc. IEEE R&A, Minneapolis, Minnesota, pp. 1744–1749.Google Scholar
  32. Kurazume, R., Hirose, S., Nagata, S., and Sashida, N. 1996. Study on cooperative positioning system. In Proc. IEEE R&A, Minneapolis, Minnesota, pp. 1421–1426.Google Scholar
  33. Langer, M., Dudek, G., and Zucker, S.W. 1995. Space occupancy using multiple shadowimages. In Proc. IEEE/RSJ IROS, IEEE Press: Pittsburgh, PA. pp. 390–396.Google Scholar
  34. Marapand, S.B., Trivedi, M., Lassiter, N., and Holder, M. 1996. Motion control of cooperative robotic teams through visual observation and fuzzy logic control. In Proc. IEEE R&A, Minneapolis, Minnesota, pp. 1738–1743.Google Scholar
  35. Mataric, M. 1992. Minimizing complexity in controlling a mobile robot population. In Proc. IEEE R&A, pp. 830–835.Google Scholar
  36. Mataric, M., Nilsson, M., and Simsarian, K.T. 1995. Cooperative multi-robot box-pushing. In Proc. IEEE/RSJ IROS, Pittsburgh, PA, 3:556–561.PubMedGoogle Scholar
  37. McFarland, D. 1989. Problems of Animal Behavior, Longman Scientific and Technical, John-Wiley and Sons.Google Scholar
  38. Milios, E., Wilkes, D., Jenkin, M., and Dudek, G. 1995. Multirobot landmark-based self-location and exploration, In Proc. 3rd Int. Symposium on Intelligent Robotic Systems'95, Pisa, Italy.Google Scholar
  39. Nitz, E. and Arkin, R.C. 1993. Communication of behavioral state in multi-agent retrieval tasks. In Proc. IEEE R&A, IEEE Press: Atlanta, GA.Google Scholar
  40. Parker, L. 1993. Designing control laws for cooperative agent teams, In Proc. IEEE R&A, Atlanta, GA, 3:582–587.Google Scholar
  41. Parker, L. 1994a. ALLIANCE: An architecture for fault tolerant cooperative control of heterogeneous mobile robots, In Proc. IEEE/RSJ IROS, Munich, Germany.Google Scholar
  42. Parker, L. 1994b. Heterogeneous Multi-Robot Cooperation. Ph.D. thesis, MIT EECS.Google Scholar
  43. Parker, L.E. 1995. The effect of action recognition and robot awareness in cooperative robotic teams. In Proc. IEEE/RSJ IROS, Pittsburgh, PA, 1:212–219.Google Scholar
  44. Partridge, B.L. 1982. The structure and function of fish schools, Sci. Am., pp. 114–123.Google Scholar
  45. Rao, N.S.V., Protopopescu, V., and Manickam, N. 1996. Cooperative terrain model acquisition by a team of two or three point-robots. In Proc. IEEE R&A, Minneapolis, Minnesota, pp. 1427–1433.Google Scholar
  46. Rus, D., Donald, B., and Jennings, J. 1995. Moving furniture with teams of autonomous robots. In Proc. IEEE/RSJ IROS, Pittsburgh,PA, 1:235–242.Google Scholar
  47. Sandini, G., Lucarini, G., and Varoli, M. 1993. Gradient driven self-organizing systems. In Proc. IEEE/RSJ IROS, Yokohama, Japan, pp. 429–433.Google Scholar
  48. Sekiyama, K. and Fukuda, T. 1996. Modeling and controlling of group behavior based on self-organizing principle. In Proc. IEEE R&A, Minneapolis, Minnesota, pp. 1407–1412.Google Scholar
  49. Suzuki, S., Asama, H., Ugeka, A., Kotosaka, S., Fujita, T., Matsumoto, A., Kaetsu, H., and Endo, I. 1995. An infra-red sensory system with local communication for cooperative multiple mobile robots. In Proc. IEEE/RSJ IROS, 1:220–225, Pittsburgh, PA.Google Scholar
  50. Tanenbaum, A.S. 1988. Computer Networks, 2nd Edition, Prentice-Hall: Englewood Cliffs, NJ.Google Scholar
  51. Tinbergen, N. 1951. The Study of Instinct, Oxford University Press.Google Scholar
  52. Tinbergen, N. 1972. The Animal in its World; Explorations of an Ethologist, 1932–1972, Harvard University Press.Google Scholar
  53. Ueyama, T., Fukuda, T., and Arai, F., 1992. Configuration of communication structure for distributed intelligent robot system. In Proc. IEEE R&A, pp. 807–812.Google Scholar
  54. van Leeuwen, J., (ed.) 1990. Handbook of Theoretical Computer Science: Volume A: Algorithms and Complexity, Elsevier, Amsterdam.Google Scholar
  55. Yuta, S. and Premvuti, S. 1992. Coordinating autonomous and centralized decision making to achieve cooperative behaviors between multiple mobile robots. In Proc. IEEE/RSJ IROS, Raleigh, NC, pp. 1566–1574.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Gregory Dudek
    • 1
  • Michael R. M. Jenkin
    • 2
  • Evangelos Milios
    • 2
  • David Wilkes
    • 2
  1. 1.Centre for Intelligent Machines, McGill UniversityMontrealCanada
  2. 2.Department of Computer ScienceYork UniversityNorth YorkCanada

Personalised recommendations