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Effects of Locomotive Drift in Scale-Invariant Robotic Search Strategies

  • Carlos Garcia-SauraEmail author
  • Eduardo Serrano
  • Francisco B. Rodriguez
  • Pablo Varona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10384)

Abstract

Robots play a fundamental role in the exploration of environments that are harmful to humans or animals: robotic probes can reach deep into the earth’s crust, explore our oceans, traverse high radiation areas, navigate in outer space, etc. The harsh conditions and large amounts of uncertainty of these environments can complicate the use of global positioning systems, and in some cases robots have to depend exclusively in local information as external position landmarks are not available. Lévy walks are increasingly studied as effective solutions in these exploratory contexts. The superdiffusive (dispersive) properties of these forms of random walks are often exploited by many animal species, in particular when tackling search problems that have uncertainty. Based on experimentation with low-cost mobile robots, this work has characterized how long-term motion drift (which is inherent to search contexts that lack global positioning systems) can have an effect in the overall characteristics of Lévy trajectories. The results show that Lévy-based searches can be robust and maintain superdiffusive properties for some ranges of motion drift parameters that are closely related to the scale of the search problem. Locomotive drift seems to act effectively as a long-term truncation parameter that could be corrected or even incorporated during the design of a search task.

Keywords

Lévy flight Bio-inspired random walks Exploration Mobile robots Scale invariance Motion drift Odor sensor 

Notes

Acknowledgements

We acknowledge support from MINECO/FEDER DPI2015-65833-P, TIN2014-54580-R (http://www.mineco.gob.es/).

References

  1. 1.
    Marjovi, A., Marques, L.: Multi-robot olfactory search in structured environments. Robot. Auton. Syst. 59(11), 867–881 (2011)Google Scholar
  2. 2.
    Hu, J., Xu, J., Xie, L.: Cooperative search and exploration in robotic networks. Unmanned Syst. 01(01), 121–142 (2013)CrossRefGoogle Scholar
  3. 3.
    Sugiyama, H., Tsujioka, T., Murata, M.: Real-time exploration of a multi-robot rescue system in disaster areas. Adv. Robot. 27(17), 1313–1323 (2013)CrossRefGoogle Scholar
  4. 4.
    Hollinger, G.A., Yerramalli, S., Singh, S., Mitra, U., Sukhatme, G.S.: Distributed data fusion for multirobot search. IEEE Trans. Robot. 31(1), 55–66 (2015)CrossRefGoogle Scholar
  5. 5.
    Shlesinger, M.F., Klafter, J.: Lévy walks versus Lévy flights. In: Stanley, H.E., Ostrowsky, N. (eds.) On Growth and Form, pp. 279–283. Springer, Dordrecht (1986)CrossRefGoogle Scholar
  6. 6.
    Zaburdaev, V., Denisov, S., Klafter, J.: Lévy walks. Rev. Mod. Phys. 87(2), 483–530 (2015)CrossRefGoogle Scholar
  7. 7.
    Humphries, N.E.M., Queiroz, N., Dyer, J.R.M., Pade, N.G., Musyl, M.K., Schaefer, K.M., Fuller, D.W., Brunnschweiler, J.M., Doyle, T.K., Houghton, J.D.R., Hays, G.C., Jones, C.S., Noble, L.R., Wearmouth, V.J., Southall, E.J., Sims, D.W.: Environmental context explains Lévy and brownian movement patterns of marine predators. Nature 465(7301), 1066–1069 (2010)CrossRefGoogle Scholar
  8. 8.
    Reynolds, A.: Liberating Lévy walk research from the shackles of optimal foraging. Phys. Life Rev. 14, 59–83 (2015)CrossRefGoogle Scholar
  9. 9.
    Nurzaman, S.G., Matsumoto, Y., Nakamura, Y., Koizumi, S., Ishiguro, H.: ‘Yuragi’-based adaptive mobile robot search with and without gradient sensing: from bacterial chemotaxis to a levy walk. Adv. Robot. 25(16), 2019–2037 (2011)CrossRefGoogle Scholar
  10. 10.
    Mohanty, P.K., Parhi, D.R.: Cuckoo search algorithm for the mobile robot navigation. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds.) SEMCCO 2013. LNCS, vol. 8297, pp. 527–536. Springer, Cham (2013). doi: 10.1007/978-3-319-03753-0_47 CrossRefGoogle Scholar
  11. 11.
    Stevens, T., Chung, T.H.: Autonomous search and counter-targeting using Levy search models. In: 2013 IEEE International Conference on Robotics and Automation, pp. 3953–3960 (2013)Google Scholar
  12. 12.
    Sutantyo, D., Levi, P., Moslinger, C., Read, M.: Collective-adaptive Lévy flight for underwater multi-robot exploration. In: 2013 IEEE International Conference on Mechatronics and Automation (IEEE ICMA 2013) (2013)Google Scholar
  13. 13.
    Fioriti, V., Fratichini, F., Chiesa, S., Moriconi, C.: Levy foraging in a dynamic environment extending the Levy search. Int. J. Adv. Robot. Syst. 12(7), 98 (2015)CrossRefGoogle Scholar
  14. 14.
    Fricke, G.M., Hecker, J.P., Cannon, J.L., Moses, M.E.: Immune-inspired search strategies for robot swarms. Robotica 34(08), 1791–1810 (2016)CrossRefGoogle Scholar
  15. 15.
    Katada, Y., Nishiguchi, A., Moriwaki, K., Watakabe, R.: Swarm robotic network using Lévy flight in target detection problem. Artif. Life Robot. 21(3), 295–301 (2016)CrossRefGoogle Scholar
  16. 16.
    Mohanty, P.K., Parhi, D.R.: Optimal path planning for a mobile robot using cuckoo search algorithm. J. Exper. Theor. Artif. Intell. 28(1–2), 35–52 (2016)CrossRefGoogle Scholar
  17. 17.
    Tromer, R.M., Barbosa, M.B., Bartumeus, F., Catalan, J., da Luz, M.G.E., Raposo, E.P., Viswanathan, G.M.: Inferring Lévy walks from curved trajectories: A rescaling method. Phys. Rev. E 92(2), 22147 (2015)CrossRefGoogle Scholar
  18. 18.
    García-Saura, C., Borja Rodríguez, F., Varona, P.: Design principles for cooperative robots with uncertainty-aware and resource-wise adaptive behavior. In: Duff, A., Lepora, N.F., Mura, A., Prescott, T.J., Verschure, P.F.M.J. (eds.) Living Machines 2014. LNCS, vol. 8608, pp. 108–117. Springer, Cham (2014). doi: 10.1007/978-3-319-09435-9_10 Google Scholar
  19. 19.
    Garcia-Saura, C.: Self-calibration of a differential wheeled robot using only a gyroscope and a distance sensor. CoRR, abs/1509.02154 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Carlos Garcia-Saura
    • 1
    Email author
  • Eduardo Serrano
    • 1
  • Francisco B. Rodriguez
    • 1
  • Pablo Varona
    • 1
  1. 1.Grupo de Neurocomputación Biológica, Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

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