Planning for Multi-robot Localization

  • Paulo Pinheiro
  • Jacques Wainer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6404)


This paper will present a cooperative multi-robot localization model with planning support. Models of communication and transmission of pose estimates are constantly explored, however how the robots act on the environment is generally defined by random actions (from the localization task’s point of view). Random actions generate observations that can be useless for improving the estimate. This work describes a proposal for multi-robot localization with planning of actions. The objective is to describe a model where policies define the best action to performed by robots. The proposed model, called Model of Planned Localization - MPL, uses POMDPs to model the problems of location and specific algorithms to generate policies. We compared the MPL to a model that does not make use of planning actions. The results showed that MPL is able to estimate the positions of robots with lower number of steps, being more efficient than model compared.


Multi-robot localization POMDP Planning Markov localization 


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  1. 1.
    Cassandra, A., Kaelbling, L.P., Kurien, J.: Acting under uncertainty: Discrete bayesian models for mobile-robot navigation. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 963–972 (1996)Google Scholar
  2. 2.
    Cox, I.J.: Blanche: an experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transaction on Robotics and Automation 7(2), 193–204 (1991)CrossRefGoogle Scholar
  3. 3.
    Fox, D.: Markov Localization: A Probabilistic Framework for Mobile Robot Localization and Navigation. PhD thesis, University of Bonn, Germany, Germany (1998)Google Scholar
  4. 4.
    Fox, D., Burgard, W., Thrun, S.: Active markov localization for mobile robots. Robotics and Autonomous Systems 25, 195–207 (1998)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fox, D., et al.: A probabilistic approach to collaborative multi-robot localization. Autonomous Robots 8(3), 325–344 (2000)CrossRefGoogle Scholar
  6. 6.
    Hoffman, J., et al.: Making use of what you don’t see: negative information in markov localization. In: International Conference on Intelligent Robots and Systems, vol. 1, pp. 2947–2952 (2005)Google Scholar
  7. 7.
    Hoffmann, J., Spranger, M., Gohring, M., Jungel, M.: Exploiting the unexpected: Negative evidence modeling and proprioceptive motion modeling for improved markov localization. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 24–35. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Nourbakhsh, I., Powers, R., Birchfield, S.: Dervish: An office-navigation robot. AI Magazine 16(2), 53–60 (1995)Google Scholar
  9. 9.
    Odakura, V.: Localização de Markov para multirrobôs cooperativos. PhD thesis, Escola Politécnica, USP, São Paulo (2006)Google Scholar
  10. 10.
    Odakura, V., Costa, A.H.R.: Cooperative multi-robot localization: using communication to reduce localization error. In: International Conference on Informatics in Control Automation and Robotics - ICINCO, vol. 3, pp. 88–93 (2005)Google Scholar
  11. 11.
    Odakura, V., Costa, A.H.R.: Negative information in cooperative multirobot localization. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds.) IBERAMIA 2006 and SBIA 2006. LNCS (LNAI), vol. 4140, pp. 552–561. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Romero, L., Arellano, J.J.: Robust local localization of a mobile robot using a 180 2-d laser range finder. In: Proceedings of the Sixth Mexican International Conference on Computer Science, vol. 1, pp. 248–255. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  13. 13.
    Romero, L., Lara, C.: Robust local localization of a mobile robot in indoor environments using virtual corners. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 901–910. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    Roumeliotis, S., Bekey, G.A.: Distributed multirobot localization. IEEE Transactions on Robotics and Automation 18, 781–795 (2002)CrossRefGoogle Scholar
  15. 15.
    Tardos, J.D., et al.: Robust mapping and localization in indoor environments using sonar. The International Journal of Robotics Research 21(4), 311–330 (2002)CrossRefGoogle Scholar
  16. 16.
    Theocharous, G., Murphy, K., Pack, L.: Representing hierarchical pomdps as dbns for multi-scale robot localization. In: International Conference on Robotics and Automation, vol. 1, pp. 1045–1051 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Paulo Pinheiro
    • 1
  • Jacques Wainer
    • 1
  1. 1.Institute of ComputingUniversity of Campinas - UNICAMPCampinasBrazil

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