Negotiation of Goal Direction for Cooperative Transport

  • Alexandre Campo
  • Shervin Nouyan
  • Mauro Birattari
  • Roderich Groß
  • Marco Dorigo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

In this paper, we study the cooperative transport of a heavy object by a group of robots towards a goal. We investigate the case in which robots have partial and noisy knowledge of the goal direction and can not perceive the goal itself. The robots have to coordinate their motion to apply enough force on the object to move it. Furthermore, the robots should share knowledge in order to collectively improve their estimate of the goal direction and transport the object as fast and as accurately as possible towards the goal.

We propose a bio-inspired mechanism of negotiation of direction that is fully distributed. Four different strategies are implemented and their performances are compared on a group of four real robots, varying the goal direction and the level of noise. We identify a strategy that enables efficient coordination of motion of the robots. Moreover, this strategy lets the robots improve their knowledge of the goal direction. Despite significant noise in the robots’ communication, we achieve effective cooperative transport towards the goal and observe that the negotiation of direction entails interesting properties of robustness.

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References

  1. 1.
    Dorigo, M., Şahin, E.: Swarm robotics – special issue editorial. Autonomous Robots 17(2–3), 111–113 (2004)CrossRefGoogle Scholar
  2. 2.
    Cao, Y.U., Fukunaga, A.S., Kahng, A.B.: Cooperative mobile robotics: Antecedents and directions. Autonomous Robots 4(1), 7–27 (1997)CrossRefGoogle Scholar
  3. 3.
    Groß, R., Mondada, F., Dorigo, M.: Transport of an object by six pre-attached robots interacting via physical links. In: Proc. of the 2006 IEEE Int. Conf. on Robotics and Automation, pp. 1317–1323. IEEE Computer Society Press, Los Alamitos (2006)CrossRefGoogle Scholar
  4. 4.
    Dieter, F., Wolfram, B., Hannes, K., Sebastian, T.: A probabilistic approach to collaborative multi-robot localization. Autonomous Robots 8(3), 325–344 (2000)CrossRefGoogle Scholar
  5. 5.
    Borenstein, J., Feng, L.: Measurement and correction of systematic odometry errors in mobile robots. IEEE Trans. on Robotics and Automation 12(5), 869–880 (1996)CrossRefGoogle Scholar
  6. 6.
    Aoki, I.: A simulation study on the schooling mechanism in fish. Bulletin of the Japanese Society of Scientific Fisheries 48(8), 1081–1088 (1982)MathSciNetGoogle Scholar
  7. 7.
    Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model. Computer Graphics 21(4), 25–34 (1987)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Huth, A., Wissel, C.: The simulation of the movement of fish schools. Journal of Theoretical Biology 156, 365–385 (1992)CrossRefGoogle Scholar
  9. 9.
    Couzin, I.D., Krause, J., James, R., Ruxton, G.D., Franks, N.R.: Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology 218(1), 1–11 (2002)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Dorigo, M., Tuci, E., Groß, R., Trianni, V., Labella, T., Nouyan, S., Ampatzis, C., Deneubourg, J.L., Baldassarre, G., Nolfi, S., Mondada, F., Floreano, D., Gambardella, L.: The SWARM-BOTS project. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics 2004. LNCS, vol. 3342, pp. 31–44. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Dorigo, M.: Swarm-bot: An experiment in swarm robotics. In: Arabshahi, P., Martinoli, A. (eds.) Proceedings of SIS 2005 – 2005 IEEE Swarm Intelligence Symposium, pp. 192–200. IEEE Press, Piscataway (2005)CrossRefGoogle Scholar
  12. 12.
    Jammalamadaka, S.R., SenGupta, A.: Topics in Circular Statistics. World Scientific Press, Singapore (2001)MATHCrossRefGoogle Scholar
  13. 13.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2005)Google Scholar
  14. 14.
    Chambers, J.M., Cleveland, W.S., Kleiner, B., Tukey, P.A.: Graphical Methods for Data Analysis. The Wadsworth statistics / probability series. Wadsworth and Brooks/Cole, Pacific Grove, CA (1983)Google Scholar
  15. 15.
    Trianni, V., Nolfi, S., Dorigo, M.: Cooperative hole avoidance in a swarm-bot. Robotics and Autonomous Systems 54(2), 97–103 (2006)CrossRefGoogle Scholar
  16. 16.
    Nouyan, S., Groß, R., Bonani, M., Mondada, F., Dorigo, M.: Group transport along a robot chain in a self-organised robot colony. In: Proc. of the 9th Int. Conf. on Intelligent Autonomous Systems, pp. 433–442. IOS Press, Amsterdam, The Netherlands (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alexandre Campo
    • 1
  • Shervin Nouyan
    • 1
  • Mauro Birattari
    • 1
  • Roderich Groß
    • 1
  • Marco Dorigo
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

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