Collective Perception of Environmental Features in a Robot Swarm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9882)

Abstract

In order to be effective, collective decision-making strategies need to be not only fast and accurate, but sufficiently general to be ported and reused across different problem domains. In this paper, we propose a novel problem scenario, collective perception, and use it to compare three different strategies: the DMMD, DMVD, and DC strategies. The robots are required to explore their environment, estimate the frequency of certain features, and collectively perceive which feature is the most frequent. We implemented the collective perception scenario in a swarm robotics system composed of 20 e-pucks and performed robot experiments with all considered strategies. Additionally, we also deepened our study by means of physics-based simulations. The results of our performance comparison in the collective perception scenario are in agreement with previous results for a different problem domain and support the generality of the considered strategies.

References

  1. 1.
    Campo, A., Garnier, S., Dédriche, O., Zekkri, M., Dorigo, M.: Self-organized discrimination of resources. PLoS ONE 6(5), e19888 (2010)CrossRefGoogle Scholar
  2. 2.
    Dorigo, M., et al.: Swarm robotics. Scholarpedia 9(1), 1463 (2014)CrossRefGoogle Scholar
  3. 3.
    Edwards, S.C., Pratt, S.C.: Rationality in collective decision-making by ant colonies. Proc. R. Soc. B 276(1673), 3655–3661 (2009)CrossRefGoogle Scholar
  4. 4.
    Gutiérrez, M., et al.: Open e-puck range & bearing miniaturized board for local communication in swarm robotics. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 3111–3116 (2009)Google Scholar
  5. 5.
    Kernbach, S., et al.: Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system. Adapt. Behav. 17(3), 237–259 (2009)CrossRefGoogle Scholar
  6. 6.
    Kornienko, S., et al.: Cognitive micro-agents: individual and collective perception in microrobotic swarm. In: Proceedings of the IJCAI 2005 Workshop on Agents in Real-Time and Dynamic Environments, Edinburgh, UK, pp. 33–42 (2005)Google Scholar
  7. 7.
    Mermoud, G., et al.: Aggregation-mediated collective perception and action in a group of miniature robots. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010, pp. 599–606. IFAAMAS (2010)Google Scholar
  8. 8.
    Mondada, F., et al.: 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. IPCB (2009)Google Scholar
  9. 9.
    Montes de Oca, M., et al.: Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intell. 5, 305–327 (2011)CrossRefGoogle Scholar
  10. 10.
    Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)CrossRefGoogle Scholar
  11. 11.
    Schmickl, T., Möslinger, C., Crailsheim, K.: Collective perception in a robot swarm. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SAB 2006 Ws 2007. LNCS, vol. 4433, pp. 144–157. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Tarapore, D., et al.: Abnormality detection in multiagent systems inspired by the adaptive immune system. In: Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2013, pp. 23–30. IFAAMAS (2013)Google Scholar
  13. 13.
    Valentini, G., et al.: Efficient decision-making in a self-organizing robot swarm: on the speed versus accuracy trade-off. In: Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, pp. 1305–1314. IFAAMAS (2015)Google Scholar
  14. 14.
    Valentini, G., Ferrante, E., Hamann, H., Dorigo, M.: Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems. Auton. Agent. Multi-Agent Syst. 30(3), 553–580 (2016)CrossRefGoogle Scholar
  15. 15.
    Valentini, G., Hamann, H., Dorigo, M.: Self-organized collective decision making: the weighted voter model. In: Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014, pp. 45–52. IFAAMAS (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium
  2. 2.Department of Computer SciencePolitecnico di MilanoMilanItaly
  3. 3.Heinz Nixdorf InstituteUniversity of PaderbornPaderbornGermany

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