Darwinian Robotic Swarms for exploration with minimal communication

Micael S. Couceiro, Rui P. Rocha, Nuno M F Ferreira, Patricia A. Vargas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

The Robotic Darwinian Particle Swarm Optimization (RDPSO) recently introduced in the literature has the ability to dynamically partition the whole population of robots based on simple 'punish-reward' rules. Although this evolutionary algorithm enables the reduction of the amount of required information exchange among robots, a further analysis on the communication complexity of the RDPSO needs to be carried out so as to evaluate its scalability. This paper analyses the architecture of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Moreover, a set of simple communication rules is also proposed in order to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots show that the proposed methodology reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.

Original languageEnglish
Title of host publication2013 IEEE Congress on Evolutionary Computation, CEC 2013
Pages127-134
Number of pages8
DOIs
Publication statusPublished - 21 Aug 2013
Event2013 IEEE Congress on Evolutionary Computation - Cancun, United Kingdom
Duration: 20 Jun 201323 Jun 2013

Conference

Conference2013 IEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2013
CountryUnited Kingdom
CityCancun
Period20/06/1323/06/13

Keywords

  • communication complexity
  • distributed search
  • MANET
  • scalability
  • swarm robotics

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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  • Cite this

    Couceiro, M. S., Rocha, R. P., Ferreira, N. M. F., & Vargas, P. A. (2013). Darwinian Robotic Swarms for exploration with minimal communication. In 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (pp. 127-134). [6557562] https://doi.org/10.1109/CEC.2013.6557562