Intelligent Service Robotics

, Volume 3, Issue 3, pp 137–149 | Cite as

A fitness-sharing based genetic algorithm for collaborative multi-robot localization

  • Andrea Gasparri
  • Stefano Panzieri
  • Attilio Priolo
Original Research Paper


In this paper, a novel genetic algorithm based on a “collaborative” fitness-sharing technique to deal with the multi-robot localization problem is proposed. Indeed, the use of the fitness-sharing is twofold and competitive. It preserves the diversity among individuals during the space exploration process, thus maintaining evolutionary niches over time, and reinforces the best hypotheses by means of collaboration among robots, thus augmenting the selection pressure. Simulations by exploiting the robotics framework Player/Stage have been performed along with a proper statistical analysis for performance assessment.


Multi-robot Localization Genetic algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cao YU, Fukunaga AS, Kahng AB (1997) Cooperative mobile robotics: antecedents and directions. Auton Robots 4(1): 7–23CrossRefGoogle Scholar
  2. 2.
    Engelson S, McDermott D (1992) Error correction in mobile robot map learning. In: IEEE ICRA, Nice, France, pp 2555–2560Google Scholar
  3. 3.
    Fox D (2003) Adapting the sample size in particle filters through kld-sampling. Int J Robot Res 22(12): 985–1004CrossRefGoogle Scholar
  4. 4.
    Fox D, Burgard W, Kruppa H, Thrun S (2000) A probabilistic approach to collaborative multi-robot localization. Auton Robot 8(3): 325–344CrossRefGoogle Scholar
  5. 5.
    Gao L, Hu Y (2006) Multi-target matching based on niching genetic algorithm. Int J Comput Sci Netw Secur 6(7): 215–220MathSciNetGoogle Scholar
  6. 6.
    Gasparri A, Panzieri S, Pascucci F, Ulivi G (2007) A spatially structured genetic algorithm over complex networks for mobile robot localization. In: IEEE ICRA, Rome, ItalyGoogle Scholar
  7. 7.
    Gasparri A, Panzieri S, Pascucci F (2009) A spatially structured genetic algorithm for multi-robot localization. Intell Serv Robot 2(1): 31–40. doi: 10.1007/s11370-008-0025-4 CrossRefGoogle Scholar
  8. 8.
    Gerkey BP, Vaughan RT, Howard A (2003) The Player/Stage project: tools for multi-robot and distributed sensor systems. In: ICAR 2003, pp 317–323Google Scholar
  9. 9.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, USAzbMATHGoogle Scholar
  10. 10.
    Howard A, Mataric MJ, Sukhatme GS (2003) Experimental robotics VIII. In: Localization for mobile robot teams: a distributed MLE approach. Springer, Berlin, pp 146–155Google Scholar
  11. 11.
    Howard A, Parker LE, Sukhatme GS (2006) The SDR experience: experiments with a large-scale heterogeneous mobile robot team. Int J Rob Res 25(5): 431–447CrossRefGoogle Scholar
  12. 12.
    Howard A (2006) Multi-robot simultaneous localization and mapping using particle filters. Int J Robot Res 25(12): 1243– 1256CrossRefGoogle Scholar
  13. 13.
    Kurazume R, Nagata S, Hirose S (1994) Cooperative positioning with multiple robots. In: IEEE ICRA, vol 2, pp 1250–1257Google Scholar
  14. 14.
    Mahfoud SW (1995) Niching methods for genetic algorithms. PhD thesis, University of Illinois at Urbana-Champaign, Champaign, IL, USAGoogle Scholar
  15. 15.
    Martinelli A, Siegwart R (2005) Observability analysis for mobile robot localization. In: IEEE IROS, pp 1264–1269Google Scholar
  16. 16.
    Martinelli A, Pont F, Siegwart R (2005) Multi-robot localization using relative observation. In: IEEE ICRAGoogle Scholar
  17. 17.
    Martinelli A, Pont F, Siegwart R (2005) Multi-robot localization using relative observations. In: Robotics and automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on April 2005, pp 2797–2802Google Scholar
  18. 18.
    Miller BL, Goldberg DE (1995) Genetic algorithms, tournament selection, and the effects of noise. Complex Syst 9: 193–212MathSciNetGoogle Scholar
  19. 19.
    Mitchell M (1998) An introduction to genetic algorithms. MIT Press, CambridgezbMATHGoogle Scholar
  20. 20.
    Mourikis A, Roumeliotis S (2006) Performance analysis of multirobot cooperative localization. IEEE Trans Robot 22(4): 666–681CrossRefGoogle Scholar
  21. 21.
    Nannen V, Smit SK, Eiben AE (2008) Costs and benefits of tuning parameters of evolutionary algorithms. In: Proceedings of the 10th international conference on parallel problem solving from nature. Springer, Berlin, pp 528–538.Google Scholar
  22. 22.
    Nerurkar ED, Roumeliotis SI, and Martinelli A (2009) Distributed maximum a posteriori estimation for multi-robot cooperative localization. In: ICRA’09: proceedings of the 2009 IEEE international conference on robotics and automation. IEEE Press, Piscataway, USA, pp 1375–1382.Google Scholar
  23. 23.
    Parker LE (2000) Current state of the art in distributed robot systems. In: Proceedings of distributed autonomous robotic systems, vol 4Google Scholar
  24. 24.
    Perez J, Pazos RA, Frausto J, Rodrguez G, Cruz L, Mora G, Fraire H (2004) Self-tuning mechanism for genetic algorithms parameters, an application to data-object allocation in the web, vol 3046, pp 77–86.Google Scholar
  25. 25.
    Rekleitis I, Dudeck G, Milios E (1997) Multi-robot exploration of an unknown environment, efficiently reducing the odometry error. In: IJCAI 1997, pp 1340–1345Google Scholar
  26. 26.
    Rekleitis IM, Dudek G, Milios EE (2002) Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy. In: IEEE IROS, pp 2690–2696Google Scholar
  27. 27.
    Rekleitis IM, Dudek G, Milios EE (2003) Probabilistic cooperative localization and mapping in practice. In: IEEE ICRA, pp 1907–1912Google Scholar
  28. 28.
    Roumeliotis SI, Bekey G (2002) Distributed multi-robot localization. IEEE Trans Robot Auton 18(5): 781–795CrossRefGoogle Scholar
  29. 29.
    Roumeliotis SI, Rekleitis IM (2004) Propagation of uncertainty in cooperative multirobot localization: Analysis and experimental results. Auton Robot 17(1): 41–54CrossRefGoogle Scholar
  30. 30.
    Thrun S, Burgard W, Fox D (2005) Probabilistic robotics intelligent robotics and autonomous agents. The MIT Press, USAGoogle Scholar
  31. 31.
    Vahdat AR, NourAshrafoddin N, Ghidary SS (1998) Mobile robot global localization using differential evolution and particle swarm optimization. In: IEEE congress on evolutionary computation, CEC 2007Google Scholar
  32. 32.
    Yuan B, Gallagher M (2005) A hybrid approach to parameter tuning in genetic algorithms, vol 2, pp 1096–1103Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Andrea Gasparri
    • 1
  • Stefano Panzieri
    • 1
  • Attilio Priolo
    • 1
  1. 1.Dipartimento di Informatica ed AutomazioneUniversità degli studi “Roma Tre”RomeItaly

Personalised recommendations