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Implementation of Mutual Localization of Multi-robot Using Particle Filter

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7389)

Abstract

This paper describes an implementation of mutual localization of swarm robot using particle filter. Robots determine the location of the other robots using wireless sensors. Measured data will be used for determination of the robot itself moving method. It also effects on the other robot’s formation such as circle and line type formation. We discuss the problem in circle formation enclosing target which moves. This method is the solution about enclosed invader in circle formation based on mutual localization of multi-robot without infrastructure. We use trilateration which does not need to know the value of the coordinates of reference points. So, specify enclosed point for the number of robots based on between the relative positions of the robot in the coordinate system. Particle filter is used to improve the accuracy of the robot’s location. The particle filter is well operated for mutual location of robots than any other estimation algorithm. Through the experiments, we show that the proposed scheme is stable and works well in real environments

Keywords

  • swarm robot
  • particle filter
  • tracking

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References

  1. Huang, D.S., Jia, W., Zhang, D.: Palmprint Verification Based on Principal Lines. Pattern Recognition 41(4), 1316–1328 (2008)

    CrossRef  Google Scholar 

  2. Huang, D.S., Lawken, K., Ip, H., Chi, Z.: Zeroing Polynomials Using Modified Constrained Neural Network Approach. IEEE Trans. on Neural Networks 16(3), 721–732 (2005)

    CrossRef  Google Scholar 

  3. Huang, D.S., Ip, H., Chi, Z.: A Neural Root Finder of Polynomials Based on Root Moments. Neural Computation 16(8), 1721–1762 (2004)

    CrossRef  MATH  Google Scholar 

  4. Huang, D.S.: A Constructive Approach for Finding Arbitary Roots of Polyminals by Neural Networks. IEEE Trans. on Neural Networks 15(2), 477–491 (2004)

    CrossRef  Google Scholar 

  5. Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)

    CrossRef  Google Scholar 

  6. Huang, D.S.: The Local Minima Free Condition of Feedforward Neural Networks for Outer Supervised Learning. IEEE Trans. on Systems, Man and Cybernetics 28B(3), 477–480 (1998)

    Google Scholar 

  7. Yeo, T.K., Hong, S., Jeon, B.H.: Latest Tendency of Underwater multi-robots. Institute of Control, Robotics and Systems 16(1), 23–34 (2010)

    Google Scholar 

  8. Arai, T., Pagello, E., Parker, L.E.: Editorial: Advances in Multi-Robot Systems. IEEE Transactions on Robotics and Automation 18(5) (2002)

    Google Scholar 

  9. Isard, M., Blake, A.: CONDENSATION-conditional Density Propagation for Visual Tracking. International Journal of Computer Vision 29(1), 5–28 (1998)

    CrossRef  Google Scholar 

  10. Lee, Y.-W.: Adaptive Data Association for Multi-target Tracking Using Relaxation. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005, Part I. LNCS, vol. 3644, pp. 552–561. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  11. Lee, Y.W., Seo, J.H., Lee, J.G.: A Study on The TWS Tracking Filter for Multi-Target Tracking. Journal of KIEE 41(4), 411–421 (2004)

    MathSciNet  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Lee, Y.W. (2012). Implementation of Mutual Localization of Multi-robot Using Particle Filter. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_12

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  • DOI: https://doi.org/10.1007/978-3-642-31588-6_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)