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A Composite Random Walk for Facing Environmental Uncertainty and Reduced Perceptual Capabilities

  • C. A. Pina-Garcia
  • Dongbing Gu
  • Huosheng Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7101)

Abstract

Theoretical and empirical studies in Biology have showed that strategies based on different random walks, such as: Brownian random walk and Lévy random walk are the best option when there is some degree of environmental uncertainty and there is a lack of perceptual capabilities.

When a random walker has no information about where targets are located, different systematic or random searches may provide different chances to find them. However, when time consumption, energy cost and malfunction risks are determinants, an adaptive search strategy becomes necessary in order to improve the performance of the strategy. Thus, we can use a practical methodology to combine a systematic search with a random search through a biological fluctuation.

We demonstrate that, in certain environments it is possible to combine a systematic search with a random search to optimally cover a given area. Besides, this work improves the search performance in comparison with pure random walks such as Brownian walk and Lévy walk. We show these theoretical results using computer simulations.

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References

  1. 1.
    James, A., Plank, M.J., Brown, R.: Optimizing the encounter rate in biological interactions: ballistic versus Lévy versus Brownian strategies. Physical Review E 78(5), 51128 (2008)CrossRefGoogle Scholar
  2. 2.
    Viswanathan, G.M., Buldyrev, S.V., Havlin, S., Da Luz, M.G.E., Raposo, E.P., Stanley, H.E.: Optimizing the success of random searches. Nature 401(6756), 911–914 (1999)CrossRefGoogle Scholar
  3. 3.
    Bartumeus, F., Da Luz, M.G.E., Viswanathan, G.M., Catalan, J.: Animal search strategies: a quantitative random-walk analysis. Ecology 86(11), 3078–3087 (2005)CrossRefGoogle Scholar
  4. 4.
    Nurzaman, S.G., Matsumoto, Y., Nakamura, Y., Shirai, K., Koizumi, S., Ishiguro, H.: An adaptive switching behavior between levy and Brownian random search in a mobile robot based on biological fluctuation. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1927–1934. IEEE (1927)Google Scholar
  5. 5.
    Berg, H.C.: Random walks in biology. Princeton Univ. Pr. (1993)Google Scholar
  6. 6.
    Vergassola, M., Villermaux, E., Shraiman, B.I.: ‘Infotaxis’ as a strategy for searching without gradients. Nature 445(7126), 406–409 (2007)CrossRefGoogle Scholar
  7. 7.
    Plank, M.J., Codling, E.A.: Sampling rate and misidentification of Lévy and non-Lévy movement paths. Ecology 90(12), 3546–3553 (2009)CrossRefGoogle Scholar
  8. 8.
    Codling, E.A., Bearon, R.N., Thorn, G.J.: Diffusion about the mean drift location in a biased random walk. Ecology 91(10), 3106–3113 (2010)CrossRefGoogle Scholar
  9. 9.
    Bénichou, O., Coppey, M., Moreau, M., Suet, P.H., Voituriez, R.: Optimal search strategies for hidden targets. Physical review letters 94(19), 198101 (2005)CrossRefGoogle Scholar
  10. 10.
    Bénichou, O., Coppey, M., Moreau, M., Voituriez, R.: Intermittent search strategies: When losing time becomes efficient. EPL (Europhysics Letters) 75, 349 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bell, W.J.: Searching behaviour: the behavioural ecology of finding resources. Chapman and Hall Ltd. (1991)Google Scholar
  12. 12.
    Fink, T., Mao, Y.: Tie knots, random walks and topology. Physica A: Statistical Mechanics and its Applications 276(1-2), 109–121 (2000)CrossRefGoogle Scholar
  13. 13.
    Fink, T.M., Mao, Y.: Designing tie knots by random walks. Nature 398(6722), 31–32 (1999)CrossRefGoogle Scholar
  14. 14.
    Pina-Garcia, C.A., Gu, D.: Using Sequences of Knots as a Random Search. In: Groß, R., Alboul, L., Melhuish, C., Witkowski, M., Prescott, T.J., Penders, J. (eds.) TAROS 2011. LNCS, vol. 6856, pp. 426–427. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Wilensky, U.: NetLogo: Center for connected learning and computer-based modeling. Northwestern University (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • C. A. Pina-Garcia
    • 1
  • Dongbing Gu
    • 1
  • Huosheng Hu
    • 1
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

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