Autonomous Robots

, Volume 8, Issue 3, pp 269–292 | Cite as

Grounded Symbolic Communication between Heterogeneous Cooperating Robots

  • David Jung
  • Alexander Zelinsky

Abstract

In this paper, we describe the implementation of a heterogeneous cooperative multi-robot system that was designed with a goal of engineering a grounded symbolic representation in a bottom-up fashion. The system comprises two autonomous mobile robots that perform cooperative cleaning. Experiments demonstrate successful purposive navigation, map building and the symbolic communication of locations in a behavior-based system. We also examine the perceived shortcomings of the system in detail and attempt to understand them in terms of contemporary knowledge of human representation and symbolic communication. From this understanding, we propose the Adaptive Symbol Grounding Hypothesis as a conception for how symbolic systems can be envisioned.

cooperative robotics heterogeneous systems symbolic communication symbol grounding learning representation behavior-based mobile robots 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arkin, R.C. and Hobbs, J.D. 1992b. Dimensions of communication and social organization in multi-agent robotic systems. In Proc. Simulation of Adaptive Behavior 92, Honolulu, HI.Google Scholar
  2. Balch, T. 1997. Social entropy: Anewmetric for learning multi-robot teams. In Proc.10th International FLAIRS Conference (FLAIRS-97).Google Scholar
  3. Balch, T. and Arkin, R.C. 1994. Communication in reactive multiagent robotic systems. Autonomous Robots, 1:27–52.Google Scholar
  4. Bond, A.H. 1996. An architectural model of the primate brain, Dept. of Computer Science, University of California, Los Angeles, CA 90024-1596.Google Scholar
  5. Brooks, R.A. 1990. Elephants don't play chess. Robotics and Autonomous Systems, 6:3–15.Google Scholar
  6. Brooks, R.A. 1991. Intelligence without reason. MIT AI Lab. Memo, 1293. Prepared for Computers and Thought, IJCAI-91.Google Scholar
  7. Bruner, J.S. 1982. The organisation of action and the nature of adultinfant transaction. In The Analysis of Action, M. von Cranach and R. Harr´e (Eds.), Cambridge University Press: Cambridge, pp. 313–328.Google Scholar
  8. Cao, Y.U., Fukunaga, A.S., Kahng, A.B., and Meng, F. 1995. Cooperative mobile robotics: Antecedents and directions. IEEE 0-8186-7108-4/95.Google Scholar
  9. Cheney, D.L. and Seyfarth, R.M. 1990. How Monkeys See theWorld, University of Chicago Press.Google Scholar
  10. Cheng, G. and Zelinsky, A. 1996. Real-time visual behaviours for navigating a mobile robot. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol. 2, pp. 973.Google Scholar
  11. Crespi, B.J. and Choe, J.C. (Eds.) 1997. The Evolution of Social Behaviour in Insects and Arachnids, Cambridge University Press: Cambridge.Google Scholar
  12. Deacon, T. 1997. The Symbolic Species: The Co-Evolution of Language and the Human Brain, Penguin Books. ISBN 0-713-99188-7.Google Scholar
  13. Dennett, D.C. 1993. Consciousness Explained, Penguin Books. ISBN-14-01.2867-0.Google Scholar
  14. Dudek, G., Jenkin, M., Milios, E., and Wilkes D. 1993. A taxonomy for swarm robots. In Proc.International Conference on Intelligent Robots and Systems (IROS), San Francisco, CA, pp. 1151–1156.Google Scholar
  15. Ford, K. and Hayes, P. (Eds.) 1991. Reasoning Agents in a Dynamic World: The Frame Problem, JAI Press.Google Scholar
  16. Gibson, J.J. 1986. The Ecological Approach to Visual Perception, Lawrence Erlbaum Associates: London. ISBN 0-89859-958-X.Google Scholar
  17. Hendriks-Jansen, H. 1996. Catching Ourselves in the Act, A Bradford Book, MIT Press: Cambridge, Massachusetts. ISBN 0-262-08246-2.Google Scholar
  18. Johnson, M. 1991. Knowing through the body, Philosophical Psychology, 4:3–18.Google Scholar
  19. Jung, D. 1998. An architecture for cooperation among autonomous agents. Ph.D. Thesis, Intelligent Robotics Laboratory, University of Wollongong, Australia. For further information on this research see the web site http://pobox.com/~david.jung/thesis.html.Google Scholar
  20. Jung, D., Heinzmann, J., and Zelinsky, A. 1998. Range and pose estimation for visual servoing on a mobile robotic target. In Proc.IEEE International Conference on Robotics and Automation (ICRA), Leuven, Belgium, Vol. 2, pp. 1226–1231.Google Scholar
  21. Jung, D. and Zelinsky, A. 1996. Whisker-based mobile robot navigation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol. 2, pp. 497–504.Google Scholar
  22. Jung, D. and Zelinsky, A. 1999a. An architecture for distributed cooperative planning in a behaviour-based multi-robot system. Journal of Robotics and Autonomous Systems (RA&S), 26:149–174.Google Scholar
  23. Jung, D. and Zelinsky, A. 1999b. Integrating spatial and topological navigation in a behavior-based multi-robot application. In proceedings of the International Conference on Intelligent Robots and Systems (IROS99), Kyongju, Korea, pp. 323–328.Google Scholar
  24. Kaiser, D. and Losick, R. 1993. How and why bacteria talk to each other. In Cell, 73(5):875–885.Google Scholar
  25. Kohonen, T. 1990. The self-organising map. Proceedings of IEEE, 78(9):1464–1479.Google Scholar
  26. Kube, C.R. Zhang, Hong 1994. Collective robotics: From social insects to robots. Adaptive Behaviour, 2(2):189–218.Google Scholar
  27. Lakoff, G. and Johnson, M. 1980. Metaphors we Live By, Chicago University Press: Chicago.Google Scholar
  28. Maes, P. 1991. Situated agents can have goals. In Designing Autonomous Agents, P. Maes (Ed.), MIT-Bradford Press. ISBN 0-262-63135-0. Also published as a special issue of the Journal for Robotics and Autonomous Systems, Vol. 6, No 1. North-Holland.Google Scholar
  29. Mallot, H.A. 1995. Layered computation in neural networks. In The Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), MIT Press, Bradford Books, p. 513, ISBN 0-262-01148-4.Google Scholar
  30. Matari´c, M.J. 1992a. Integration of representation into goal-driven behavior-based robots. In IEEE Transactions on Robotics and Automation, 8(3):304–312.Google Scholar
  31. Matari´c, M.J. 1992b. Behavior-based systems: Key properties and implications. In Proceedings, IEEE International Conference on Robotics and Automation, Workshop on Architectures for Intelligent Control Systems, Nice, France, pp. 46–54.Google Scholar
  32. Matari´c, M.J. 1997. Using communication to reduce locality in distributed multi-agent learning. In Proceedings, AAAI-97, Providence, Rhode Island, July 27–31, pp. 643–648.Google Scholar
  33. Mills, C.W. 1940. Situated actions and vocabularies of motive. American Sociological Review, 5:904–913.Google Scholar
  34. Newell, A. and Simon, H.A. 1972. Human Problem Solving, Prentice-Hall, Inc: Englewood Cliffs, NJ.Google Scholar
  35. Parker, L.E. 1995. The effect of action recognition and robot awareness in cooperative robotic teams. IEEE, 0-8186-7108-4/95.Google Scholar
  36. Pasteels, J.M., Deneubourg, J., and Goss, S. 1987. Self-organization mechanisms in ant societies: Trail recruitement to newly discovered food sources. In From individual to Collective Behavior in Social Insects, J.M. Pasteels and J. Deneubourg (Eds.), Birk¨auser Verlag: Basel.Google Scholar
  37. Pfeifer, R. 1995. Cognition—Perspectives from autonomous agents. Robotics and Autonomous Systems, 15:47–70.Google Scholar
  38. yshym, Z.W. (Ed.) 1987. The Robot's Dilemma.The Frame Problem in Artifical Intelligence, Theoretical Issues on Cognitive Science, Vol. 4, Ablex Pub Corp.Google Scholar
  39. Robins, R.H. 1997. A Short History of Linguistics, 4th ed. (Longman Linguistics Library), Addison-Wesley Pub Co. ISBN: 0582249945.Google Scholar
  40. Shepard, R.N. and Cooper, L.A. 1982. Mental Images and their Transformations, MIT Press/Bradford: Cambridge.Google Scholar
  41. Stains, H.J. 1984. Carnivores. In Orders and Families of Recent Mammals of the World, S. Anderson and J.K. Jones, Jr. (Eds.), John Wiley and Sons: NY, pp. 491–521.Google Scholar
  42. Steels, L.1996. The origins of intelligence, In Proceedings of the Carlo Erba Foundation, Meeting on Artificial Life, Fondazione Carlo Erba. Milano.Google Scholar
  43. Suchman, L.A. 1987. Plans and Situated Actions: The Problem of Human-Machine Communication, Cambridge Press: Cambridge.Google Scholar
  44. Thelen, E. and Smith, L.B. 1994. A Dynamic Systems Approach to the Development of Cognition and Action, A Bradford book, MIT Press. ISBN 0-262-20095-3.Google Scholar
  45. Wilson, E.O. 1971. The Insect Societies: Their Origin and Evolution, Narcourt, Brace & Co: New York.Google Scholar
  46. Wilson, E.O. 1975. Sociobiology: The New Synthesis, Harvard.Google Scholar
  47. Yuta, S., Suzuki, S., and Iida, S. 1991. Implementation of a small size experimental self-contained autonomous robot—sensors, vehicle control, and description of sensor based behavior. In Proc. Experimental Robotics, Tolouse, France, LAAS/CNRS.Google Scholar
  48. Zelinsky, A., Kuniyoshi, Y., and Tsukue, H. 1993. A qualitative approach to achieving robust performance by a mobile agent, Robotics Society of Japan Conference, Japan.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • David Jung
    • 1
  • Alexander Zelinsky
    • 2
  1. 1.Center for Engineering Science Advanced Research (CESAR), Computer Science and Mathematics Division (CSMD)Oak Ridge National LaboratoryOak RidgeUSA
  2. 2.Robotic Systems Laboratory (RSL), Department of Systems Engineering, Research School of Information Sciences and EngineeringThe Australian National UniversityCanberraAustralia

Personalised recommendations