Skip to main content

General detection model in cooperative multirobot localization

Abstract

The cooperative multirobot localization problem consists in localizing each robot in a group within the same environment, when robots share information in order to improve localization accuracy. It can be achieved when a robot detects and identifies another one, and measures their relative distance. At this moment, both robots can use detection information to update their own poses beliefs. However some other useful information besides single detection between a pair of robots can be used to update robots poses beliefs as: propagation of a single detection for non participants robots, absence of detections and detection involving more than a pair of robots. A general detection model is proposed in order to aggregate all detection information, addressing the problem of updating poses beliefs in all situations depicted. Experimental results in simulated environment with groups of robots show that the proposed model improves localization accuracy when compared to conventional single detection multirobot localization.

References

  1. 1.

    Boyen X and Koller D. Tractable inference for complex stochastic processes. In:Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence; 1998; Madison, Wisconsin. San Francisco: Morgan Kaufmann; 1998. p. 33–42.

    Google Scholar 

  2. 2.

    Fox D, Burgard W, Kruppa H and Thrun S. A probabilistic approach to collaborative multi-robot localization.Autonomous Robots 2000; 8(3): 325–344.

    Article  Google Scholar 

  3. 3.

    Fox D, Burgard W and Thrun S. Markov localization for mobile robots in dynamic environments.Journal of Artificial Intelligence Research 1999; 11:391–427.

    MATH  Google Scholar 

  4. 4.

    Howard A, Mataric M and Sukhatme G. Putting the “i” in “team”: an egocentric approach to cooperative localization. In:Proceedings of IEEE International Conference on Robotics and Automation; 2000; Taipei, Taiwan. New York: Kluwer Academic Publishers; 2003. p. 14–19.

    Google Scholar 

  5. 5.

    Leonard J and Durrant-Whyte H. Mobile robot localization by tracking geometric beacons.IEEE Transactions on Robotics and Automation 1991; 7(3):376–382.

    Article  Google Scholar 

  6. 6.

    Lima PU. Bayesian approach to sensor fusion in autonomous sensor and robot networks.IEEE Instrumentation and Measurement Magazine 2007; 10(3):22–17.

    Article  Google Scholar 

  7. 7.

    Lowe D. Distinctive image features from scale invariant keypoints.International Journal of Computer Vision 2004; 60(2):91–110.

    Article  Google Scholar 

  8. 8.

    Odakura V and Costa AHR. Cooperative multi-robot localization: using communication to reduce localization error. In:Proceedings of International Conference on Informatics in Control, Automation and Robotics; 2005; Barcelona. Portugal: INSTICC Press; 2005. p. 88–93.

    Google Scholar 

  9. 9.

    Odakura V and Costa AHR. Multidetection in multirobot cooperative localization. In:Proceedings of First IFAC Workshop on Multivehicle Systems; 2006; Salvador. São José dos Campos: Instituto Tecnológico de Aeronáutica (Biblioteca Central); 2006. p. 19–24.

    Google Scholar 

  10. 10.

    Odakura V and Costa AHR. Negative information in cooperative multirobot localization. In:Proceedings of Advances in Artificial Intelligence; 2006; Ribeirão Preto, São Paulo. New York: Springer; 2006. p. 552–561. (v. LNAI 4140).

    Google Scholar 

  11. 11.

    Odakura V, Sacchi R, Ramisa A, Bianchi R and Costa AHR. The use of negative detection in cooperative localization in a team of four-legged robots. In:Anais do Simpósio Brasileiro de Automação Inteligente; 2009; Brasília. Porto Alegre: SBA; 2009.

    Google Scholar 

  12. 12.

    RoboCup Technical Committee.RoboCup Four-Legged League Rule Book. Switzerland: The RoboCup Federation; 2006.

    Google Scholar 

  13. 13.

    Roumeliotis SI and Bekey GA. Distributed multirobot localization.IEEE Transactions on Robotics and Automation 2002; 18(2):781–795.

    Article  Google Scholar 

  14. 14.

    Russell S and Norvig P.Artificial Intelligence: a modern approach. New Jersey: Prentice Hall; 1995.

    MATH  Google Scholar 

Download references

Author information

Affiliations

Authors

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Odakura, V.V.V.A., da Costa Bianchi, R.A. & Costa, A.H.R. General detection model in cooperative multirobot localization. J Braz Comp Soc 15, 33–46 (2009). https://doi.org/10.1007/BF03194504

Download citation

Keywords

  • multirobot
  • probabilistic localization
  • detection model