Collective Perception of Environmental Features in a Robot Swarm

  • Gabriele ValentiniEmail author
  • Davide Brambilla
  • Heiko Hamann
  • Marco DorigoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9882)


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.


Quality Estimate Dissemination State Voter Model Exploration State Swarm Size 
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.



The authors would like to thank A. Reina, L. Garattoni, and A. Antoun for their assistance during the development of this study.


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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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