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Comparison of Different Cue-Based Swarm Aggregation Strategies

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8794)

Abstract

In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naïve with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naïve method.

Keywords

swarm robotics collective behavior cue-based aggregation 

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References

  1. 1.
    Camazine, S., Franks, N., Sneyd, J., Bonabeau, E., Deneubourg, J.L., Theraulaz, G.: Self-organization in biological systems. Princeton University Press (2003)Google Scholar
  2. 2.
    Parrish, J., Edelstein-Keshet, L.: Complexity, pattern, and evolutionary trade-offs in animal aggregation. Science 284(5411), 99–101 (1999)CrossRefGoogle Scholar
  3. 3.
    Jeanson, R., Rivault, C., Deneubourg, J.L., Blanco, S., Fournier, R., Jost, C., Theraulaz, G.: Self-organized aggregation in cockroaches. Animal Behaviour 69(1), 169–180 (2005)CrossRefGoogle Scholar
  4. 4.
    Şahin, E., Girgin, S., Bayındır, L., Turgut, A.E.: Swarm robotics. In: Blum, C., Merkle, D. (eds.) Swarm Intelligence, vol. 1, pp. 87–100. Springer, Heidelberg (2008)Google Scholar
  5. 5.
    Kube, C., Zhang, H.: Collective robotics: From social insects to robots. Adaptive Behavior 2(2), 189–219 (1993)CrossRefGoogle Scholar
  6. 6.
    Melhuish, C., Holland, O., Hoddell, S.: Convoying: Using chorusing to form travelling groups of minimal agents. Robotics and Autonomous Systems 28(2), 207–216 (1999)CrossRefGoogle Scholar
  7. 7.
    Schmickl, T., Thenius, R., Moeslinger, C., Radspieler, G., Kernbach, S., Szymanski, M., Crailsheim, K.: Get in touch: Cooperative decision making based on robot-to-robot collisions. Autonomous Agents and Multi-Agent Systems 18(1), 133–155 (2009)CrossRefGoogle Scholar
  8. 8.
    Kernbach, S., Thenius, R., Kernbach, O., Schmickl, T.: Re-embodiment of Honeybee Aggregation Behavior in an Artificial Micro-Robotic System. Adaptive Behavior 17(3), 237–259 (2009)CrossRefGoogle Scholar
  9. 9.
    Schmickl, T., Hamann, H.: Beeclust: A swarm algorithm derived from honeybees. In: Xiao, Y., Hu, F. (eds.) Bio-inspired Computing and Communication Networks (2010)Google Scholar
  10. 10.
    Bodi, M., Thenius, R., Szopek, M., Schmickl, T., Crailsheim, K.: Interaction of robot swarms using the honeybee-inspired control algorithm beeclust. Mathematical and Computer Modelling of Dynamical Systems 18(1), 87–100 (2012)CrossRefzbMATHGoogle Scholar
  11. 11.
    Kengyel, D., Thenius, R., Crailsheim, K., Schmick, T.: Influence of a social gradient on a swarm of agents controlled by the beeclust algorithm. In: European Conference on Artificial Life, pp. 1041–1048 (2013)Google Scholar
  12. 12.
    Hereford, J.: Beeclust swarm algorithm: Analysis and implementation using a markov chain model. International Journal of Innovative Computing and Applications 5(2), 115–124 (2013)CrossRefGoogle Scholar
  13. 13.
    Arvin, F., Samsudin, K., Ramli, A.R., Bekravi, M.: Imitation of honeybee aggregation with collective behavior of swarm robots. International Journal of Computational Intelligence Systems 4(4), 739–748 (2011)Google Scholar
  14. 14.
    Arvin, F., Turgut, A.E., Yue, S.: Fuzzy-based aggregation with a mobile robot swarm. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 346–347. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Arvin, F., Turgut, A.E., Bazyari, F., Arikan, K.B., Bellotto, N., Yue, S.: Cue-based aggregation with a mobile robot swarm: A novel fuzzy-based method. Adaptive Behavior 22(3), 189–206 (2014)CrossRefGoogle Scholar
  16. 16.
    Trianni, V., Groß, R., Labella, T.H., Şahin, E., Dorigo, M.: Evolving aggregation behaviors in a swarm of robots. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 865–874. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Soysal, O., Şahin, E.: Probabilistic aggregation strategies in swarm robotic systems. In: Swarm Intelligence Symposium, pp. 325–332. IEEE (2005)Google Scholar
  18. 18.
    Soysal, O., Bahçeci, E., Şahin, E.: Aggregation in swarm robotic systems: Evolution and probabilistic control. Turkish Journal of Electrical Engineering & Computer Sciences 15(2), 199–225 (2007)Google Scholar
  19. 19.
    Bayindir, L., Şahin, E.: Modeling self-organized aggregation in swarm robotic systems. In: Swarm Intelligence Symposium, pp. 88–95. IEEE (2009)Google Scholar
  20. 20.
    Arvin, F., Samsudin, K., Ramli, A.R.: Development of a Miniature Robot for Swarm Robotic Application. International Journal of Computer and Electrical Engineering 1, 436–442 (2009)CrossRefGoogle Scholar
  21. 21.
    Arvin, F., Bekravi, M.: Encoderless position estimation and error correction techniques for miniature mobile robots. Turkish Journal of Electrical Engineering & Computer Sciences 21, 1631–1645 (2013)CrossRefGoogle Scholar
  22. 22.
    Arvin, F., Samsudin, K., Ramli, A.R.: Development of IR-Based Short-Range Communication Techniques for Swarm Robot Applications. Advances in Electrical and Computer Engineering 10(4), 61–68 (2010)CrossRefGoogle Scholar
  23. 23.
    Hamann, H.: Towards swarm calculus: Universal properties of swarm performance and collective decisions. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 168–179. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computational Intelligence Lab (CIL), School of Computer ScienceUniversity of LincolnLincolnUK
  2. 2.Laboratory of Socioecology and Social EvolutionKU LeuvenLeuvenBelgium

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