Chain Based Path Formation in Swarms of Robots

  • Shervin Nouyan
  • Marco Dorigo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)


In this paper we analyse a previously introduced swarm intelligence control mechanism used for solving problems of robot path formation. We determine the impact of two probabilistic control parameters. In particular, the problem we consider consists in forming a path between two objects which an individual robot cannot perceive simultaneously.

Our experiments were conducted in simulation. We compare four different robot group sizes with up to 20 robots, and vary the difficulty of the task by considering five different distances between the objects which have to be connected by a path.

Our results show that the two investigated parameters have a strong impact on the behaviour of the overall system and that the optimal set of parameters is a function of group size and task difficulty. Additionally, we show that our system scales well with the number of robots.


Completion Time Exploration Rate Motor Schema Single Robot Swarm Robotic 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Filliat, D., Meyer, J.A.: Map-based navigation in mobile robots - I. A review of localization strategies. J. of Cognitive Systems Research 4, 243–282 (2003)CrossRefGoogle Scholar
  2. 2.
    Meyer, J.A., Filliat, D.: Map-based navigation in mobile robots - II. A review of map-learning and path-planning strategies. J. of Cognitive Systems Research 4, 283–317 (2003)CrossRefGoogle Scholar
  3. 3.
    Howard, A.: Multi-robot mapping using manifold representations. In: Proc. of the 2004 IEEE Int. Conf. on Robotics and Automation, pp. 4198–4203. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar
  4. 4.
    Deneubourg, J.L., Aron, S., Goss, S., Pasteels, J.M.: The self-organizing exploratory pattern of the argentine ant. J. Insect Behavior 3, 159–168 (1990)CrossRefGoogle Scholar
  5. 5.
    Goss, S., Deneubourg, J.L.: Harvesting by a group of robots. In: Proc. of the 1st European Conf. on Artificial Life, pp. 195–204. MIT Press, Cambridge (1992)Google Scholar
  6. 6.
    Drogoul, A., Ferber, J.: From Tom Thumb to the dockers: Some experiments with foraging robots. In: From Animals to Animats 2. Proc. of the 2nd Int. Conf. on Simulation of Adaptive Behavior (SAB 1992), pp. 451–459. MIT Press, Cambridge (1992)Google Scholar
  7. 7.
    Werger, B., Matarić, M.: Robotic food chains: Externalization of state and program for minimal-agent foraging. In: From Animals to Animats 4, Proc. of the 4th Int. Conf. on Simulation of Adaptive Behavior (SAB 1996), pp. 625–634. MIT Press, Cambridge (1996)Google Scholar
  8. 8.
    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
  9. 9.
    Mondada, F., Gambardella, L.M., Floreano, D., Nolfi, S., Deneubourg, J.L., Dorigo, M.: The cooperation of swarm-bots: Physical interactions in collective robotics. IEEE Robotics & Automation Magazine 12(2), 21–28 (2005)CrossRefGoogle Scholar
  10. 10.
    Christensen, A.L.: Efficient neuro-evolution of hole-avoidance and phototaxis for a swarm-bot. Technical Report TR/IRIDIA/2005-14, Université Libre de Bruxelles, Belgium, DEA Thesis (2005)Google Scholar
  11. 11.
    Arkin, R.: Behavior-Based Robotics. MIT Press, Cambridge (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shervin Nouyan
    • 1
  • Marco Dorigo
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

Personalised recommendations