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A spatially structured genetic algorithm for multi-robot localization

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Abstract

In this paper the multi-robot localization problem is addressed. A new framework based on a spatially structured genetic algorithm is proposed. Collaboration among robots is considered and is limited to the exchange of sensor data. Additionally, the relative distance and orientation among robots are assumed to be available. The proposed framework (MR-SSGA) takes advantage of the cooperation so that the perceptual capability of each robot is extended. Cooperation can be set-up at any time when robots meet, it is fully decoupled and does not require robots to stop. Several simulations have been performed, either considering cooperation activated or not, in order to emphasize the effectiveness of the collaboration strategy.

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References

  1. Parker LE (2000) Current state of the art in distributed robot systems. In: Proceedings of distributed autonomous robotic systems, vol 4

  2. Kurazume R, Nagata S, Hirose S (1994) Cooperative positioning with multiple robots. In: IEEE international conference on robotics and automation

  3. Rekleitis IM, Dudek G, Milios EE (2002) Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy. In: IEEE/RSJ international conference on intelligent robot and system

  4. Rekleitis IM, Dudek G, Milios EE (2003) Probabilistic cooperative localization and mapping in practice. In: IEEE international conference on robotics and automation

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

    Article  Google Scholar 

  6. Roumeliotis SI, Bekey GA (2002) Distributed multi-robot localization. IEEE Trans Robot Autom 18(5): 781–795

    Article  Google Scholar 

  7. Mourikis AI, Roumeliotis SI (2006) Performance analysis of multi-robot Cooperative localization. IEEE Trans Robot 22(4): 666–681

    Article  Google Scholar 

  8. Martinelli A, Pont F, Siegwart R (2005) Multi-robot localization using relative observation. In: IEEE international conference on robotics and automation

  9. Martinelli A, Siegwart R (2005) Observability analysis for mobile robot localization. In: IEEE/RSJ international conference on intelligent robot and system

  10. Gasparri A, Panzieri S, Pascucci F, Ulivi G (2007) A spatially structured genetic algorithm over complex networks for mobile robot localization. In: IEEE international conference on robotics and automation

  11. Tomassini M (2005) Spatially structured evolutionary algorithms: artificial evolution in space and time (natural computing series). Springer, New York

    MATH  Google Scholar 

  12. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New York

    MATH  Google Scholar 

  13. Ashlock D, Smucker M, Walker J (1999) Graph Based Genetic Algorithms. In: Congress on evolutionary computation

  14. Barabási AL, Reka A (1999) Emergence of scaling in random networks. Science 286: 509–512

    Article  MathSciNet  Google Scholar 

  15. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442

    Article  Google Scholar 

  16. Wang XF, Chen G (2003) Complex networks: small-world, scale-free and beyond. IEEE Circuits Syst Mag 3(1): 6–20

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Dip. Informatica e Automazione, Università degli Studi Roma Tre, Via della Vasca Navale 79, 00146, Roma, Italy

    Andrea Gasparri, Stefano Panzieri & Federica Pascucci

Authors
  1. Andrea Gasparri
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  2. Stefano Panzieri
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  3. Federica Pascucci
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Corresponding author

Correspondence to Stefano Panzieri.

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Gasparri, A., Panzieri, S. & Pascucci, F. A spatially structured genetic algorithm for multi-robot localization. Intel Serv Robotics 2, 31–40 (2009). https://doi.org/10.1007/s11370-008-0025-4

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  • DOI: https://doi.org/10.1007/s11370-008-0025-4

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