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
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)
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)
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)
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)
Berg, H.C.: Random walks in biology. Princeton Univ. Pr. (1993)
Vergassola, M., Villermaux, E., Shraiman, B.I.: ‘Infotaxis’ as a strategy for searching without gradients. Nature 445(7126), 406–409 (2007)
Plank, M.J., Codling, E.A.: Sampling rate and misidentification of Lévy and non-Lévy movement paths. Ecology 90(12), 3546–3553 (2009)
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)
Bénichou, O., Coppey, M., Moreau, M., Suet, P.H., Voituriez, R.: Optimal search strategies for hidden targets. Physical review letters 94(19), 198101 (2005)
Bénichou, O., Coppey, M., Moreau, M., Voituriez, R.: Intermittent search strategies: When losing time becomes efficient. EPL (Europhysics Letters) 75, 349 (2006)
Bell, W.J.: Searching behaviour: the behavioural ecology of finding resources. Chapman and Hall Ltd. (1991)
Fink, T., Mao, Y.: Tie knots, random walks and topology. Physica A: Statistical Mechanics and its Applications 276(1-2), 109–121 (2000)
Fink, T.M., Mao, Y.: Designing tie knots by random walks. Nature 398(6722), 31–32 (1999)
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)
Wilensky, U.: NetLogo: Center for connected learning and computer-based modeling. Northwestern University (1999)
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Pina-Garcia, C.A., Gu, D., Hu, H. (2011). A Composite Random Walk for Facing Environmental Uncertainty and Reduced Perceptual Capabilities. In: Jeschke, S., Liu, H., Schilberg, D. (eds) Intelligent Robotics and Applications. ICIRA 2011. Lecture Notes in Computer Science(), vol 7101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25486-4_62
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DOI: https://doi.org/10.1007/978-3-642-25486-4_62
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