Mobile Stigmergic Markers for Navigation in a Heterogeneous Robotic Swarm

  • Frederick Ducatelle
  • Gianni A. Di Caro
  • Alexander Förster
  • Luca Gambardella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)


We study self-organized navigation in a heterogeneous robotic swarm consisting of two types of robots: small wheeled robots, called foot-bots, and flying robots that can attach to the ceiling, called eye-bots. The task of foot-bots is to navigate back and forth between a source and a target location. The eye-bots are placed in a chain on the ceiling, connecting source and target using infrared communication. Their task is to guide foot-bots, by giving local directional instructions. The problem we address is how the positions of eye-bots and the directional instructions they give can be adapted, so that they indicate a path that is efficient for foot-bot navigation, also in the presence of obstacles. We propose an approach of mutual adaptation between foot-bots and eye-bots. Our solution is inspired by pheromone based navigation of ants, as eye-bots serve as mobile stigmergic markers for foot-bot navigation.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Batalin, M., Sukhatme, G., Hattig, M.: Mobile robot navigation using a sensor network. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (2004)Google Scholar
  2. 2.
    Bertsekas, D., Gallager, R.: Data Networks. Prentice Hall, Englewood Cliffs (1992)MATHGoogle Scholar
  3. 3.
    Ducatelle, F., Di Caro, G., Gambardella, L.: Cooperative stigmergic navigation in a heterogeneous robotic swarm. In: Proceedings of SAB (2010)Google Scholar
  4. 4.
    Fujisawa, R., Dobata, S., Kubota, D., Imamura, H., Matsuno, F.: Dependency by concentration of pheromone trail for multiple robots. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 283–290. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Momen, S., Amavasai, B., Siddique, N.: Mixed species flocking for heterogeneous robotic swarms. In: Proceedings of IEEE Eurocon (2007)Google Scholar
  6. 6.
    Momen, S., Sharkey, A.: An ant-like task allocation model for a swarm of heterogeneous robots. In: Proceedings of SIAAS (2009)Google Scholar
  7. 7.
    O’Hara, K., Balch, T.: Pervasive sensor-less networks for cooperative multi-robot tasks. In: Proceedings of DARS (2004)Google Scholar
  8. 8.
    Pinciroli, C., O’Grady, R., Christensen, A., Dorigo, M.: Self-organised recruitment in a heterogeneous swarm. In: Proceedings of ICAR (2009)Google Scholar
  9. 9.
    Stirling, T., Wischmann, S., Floreano, D.: Energy-efficient indoor search by swarms of simulated flying robots without global information. In: Swarm Intelligence (2010)Google Scholar
  10. 10.
    Sugawara, K., Kazama, T., Watanabe, T.: Foraging behavior of interacting robots with virtual pheromone. In: Proceedings of IROS (2004)Google Scholar
  11. 11.
    Vaughan, R., Støy, K., Sukhatme, G., Mataric, M.: Whistling in the dark: Cooperative trail following in uncertain localization space. In: Proc. of Autonomous Agents (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Frederick Ducatelle
    • 1
  • Gianni A. Di Caro
    • 1
  • Alexander Förster
    • 1
  • Luca Gambardella
    • 1
  1. 1.Dalle Molle Institute for Artificial Intelligence Studies (IDSIA)LuganoSwitzerland

Personalised recommendations