Evolving Aggregation Behaviors in Multi-Robot Systems with Binary Sensors

  • Melvin GauciEmail author
  • Jianing Chen
  • Tony J. Dodd
  • Roderich Groß
Conference paper
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 104)


This paper investigates a non-traditional sensing trade-off in swarm robotics: one in which each robot has a relatively long sensing range, but processes a minimal amount of information. Aggregation is used as a case study, where randomly-placed robots are required to meet at a common location without using environmental cues. The binary sensor used only lets a robot know whether or not there is another robot in its direct line of sight. Simulation results with both a memoryless controller (reactive) and a controller with memory (recurrent) prove that this sensor is enough to achieve error-free aggregation, as long as a sufficient sensing range is provided. The recurrent controller gave better results in simulation, and a post-evaluation with it shows that it is able to aggregate at least 1000 robots into a single cluster consistently. Simulation results also show that, with the recurrent controller, false negative noise on the sensor can speed up the aggregation process. The system has been implemented on 20 physical e-puck robots, and systematic experiments have been performed with both controllers: on average, 86-89% of the robots aggregated into a single cluster within 10 minutes.


Aggregation Performance Homogeneous Environment Controller Synthesis Neural Network Controller 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.
    Ando, H., Oasa, Y., Suzuki, I., Yamashita, M.: Distributed memoryless point convergence algorithm for mobile robots with limited visibility. IEEE Trans. Robotic. Autom. 15(5), 818–828 (1999)CrossRefGoogle Scholar
  2. 2.
    Bahceci, E., Şahin, E.: Evolving aggregation behaviors for swarm robotic systems: a systematic case study. In: Proc. 2005 IEEE Swarm Intelligence Symposium, pp. 333–340 (2005)Google Scholar
  3. 3.
    Camazine, S., Franks, N.R., Sneyd, J., Bonabeau, E., Deneubourg, J.-L., Theraulaz, G.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2001)Google Scholar
  4. 4.
    Chellapilla, K., Fogel, D.B.: Evolving an expert checkers playing program without human expertise. IEEE. Trans. Evolut. Comput. 5(4), 422–488 (2001)CrossRefGoogle Scholar
  5. 5.
    Correll, N., Martinoli, A.: Modeling and designing self-organized aggregation in a swarm of miniature robots. Int. J. Robot. Res. 30(5), 615–626 (2011)CrossRefGoogle Scholar
  6. 6.
    Cortés, J., Martinez, S., Bullo, F.: Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions. IEEE Trans. Automat. Contr. 51(8), 1289–1298 (2006)CrossRefGoogle Scholar
  7. 7.
    Deneubourg, J.-L., Gregoire, J.-C., Le Fort, E.: Kinetics of larval gregarious behavior in the bark beetle Dendroctonus micans (Coleoptera: Scolytidae). J. Insect. Behav. 3, 169–182 (1990)CrossRefGoogle Scholar
  8. 8.
    Dorigo, M., Trianni, V., Şahin, E., Groß, R., Labella, T.H., Baldassarre, G., Nolfi, S., Deneubourg, J.-L., Mondada, F., Floreano, D., Gambardella, L.: Evolving self-organizing behaviors for a swarm-bot. Auton. Robot. 17, 223–245 (2004)CrossRefGoogle Scholar
  9. 9.
    Garnier, S., Jost, C., Gautrais, J., Asadpour, M., Caprari, G., Jeanson, R., Grimal, A., Theraulaz, G.: The embodiment of cockroach aggregation behavior in a group of micro-robots. Artif. Life 14(4), 387–408 (2008)CrossRefGoogle Scholar
  10. 10.
    Gauci, M., Chen, J., Dodd, T.J., Groß, R.: Online supplementary material (2012),
  11. 11.
    De Gennaro, M.C., Jadbabie, A.: Decentralized control of connectivity for multi-agent systems. In: Proc. 45th IEEE Conf. Decision and Control, pp. 3628–3633 (2006)Google Scholar
  12. 12.
    Gordon, N., Wagner, I.A., Bruckstein, A.M.: Gathering multiple robotic a(ge)nts with limited sensing capabilities. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 142–153. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Graham, R.L., Sloane, N.J.A.: Penny-packing and two-dimensional codes. Dicrete Comput. Geom. 5, 1–11 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tâche, F., Saïd, I., Durier, V., Canonge, S., Amé, J.M., Detrain, C., Correll, N., Martinoli, A., Mondada, F., Siegwart, R., Deneubourg, J.L.: Social integration of robots into groups of cockroaches to control self-organized choices. Science 318, 1155–1158 (2007)CrossRefGoogle Scholar
  15. 15.
    Jeanson, R., Rivault, C., Deneubourg, J.-L., Blanco, S., Fournier, R., Jost, C., Theraulaz, G.: Self-organized aggregation in cockroaches. Anim. Behav. 69(1), 169–180 (2005)CrossRefGoogle Scholar
  16. 16.
    Kernbach, S., Thenius, R., Kernbach, O., Schmickl, T.: Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system. Adapt. Behav. 17(3), 237–259 (2009)CrossRefGoogle Scholar
  17. 17.
    Magnenat, S., Waibel, M., Beyeler, A.: Enki: The fast 2d robot simulator (2011),
  18. 18.
    Mondada, F., Bonani, M., Raemy, X., Pugh, J., Canci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, vol. 1, pp. 59–65 (2009)Google Scholar
  19. 19.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. The MIT Press, Cambridge (2000)Google Scholar
  20. 20.
    Parrish, J.K., Edelstein-Keshet, L.: Complexity, pattern, and evolutionary trade-offs in animal aggregation. Science 284(5411), 99–101 (1999)CrossRefGoogle Scholar
  21. 21.
    Trianni, V.: Evolutionary Swarm Robotics. SCI, vol. 108. Springer, Berlin (2008)Google Scholar
  22. 22.
    Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural. Comput. 1(2), 270–280 (1989)CrossRefGoogle Scholar
  23. 23.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evolut. Comput. 3(2), 82–102 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Melvin Gauci
    • 1
    Email author
  • Jianing Chen
    • 1
  • Tony J. Dodd
    • 1
  • Roderich Groß
    • 1
  1. 1.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK

Personalised recommendations