Advertisement

Multi-Agent Exploration Inside Structural Collapses

  • Panteha SaeediEmail author
  • Soren Aksel Sorensen
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 60)

Abstract

Autonomous navigation in unknown cluttered environments is one of the main challenges for search and rescue robots inside collapsed buildings. Being able to compare different search strategies in various search fields is crucial to attain fast victim localization. Thus we discuss an algorithmic development and proliferation of realistic afterdisaster test fields for search and rescue simulated robots. In this paper we characterized our developed search environments by their fractal dimensions. This index has shown to be a discriminative index for narrow pathways inside confined and cluttered spaces in our simulation test fields.

Keywords

Multi-agent exploration algorithms fractal dimensions 

References

  1. 1.
    Balch, T.: Behavioural diversity in learning robot teams. Ph.D. thesis, College of Computing, Georgia Institute of Technology, USA (1998)Google Scholar
  2. 2.
    Box, G.E.P., Muller, M.E.: A note on the generation of random normal deviates. Ann. Math. Stat. 29(2), 610–611 (1958)zbMATHCrossRefGoogle Scholar
  3. 3.
    Jacoff, A., Messina, E., Evans, J.: A reference test course for autonomous mobile robots. In: Proceeding of SPIE-AeroSense Conference, Orlando (2001)Google Scholar
  4. 4.
    Jones, A.L.: Image segmentation via fractal dimension. Technical Report, Air-Force Institute of Technology, Wright-Patterson AFB, Ohio (1987)Google Scholar
  5. 5.
  6. 6.
    Matsuo, Y., Tamura, Y.: Tree formation multi-robot system for victim search a devastated indoor space. In: Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), vol. 2, pp. 1071–1076. Sendal, Japan (2004)Google Scholar
  7. 7.
    Merloti, P.E., Lewis, J.: Simulation of artificial ants behaviour in a digital environment. In: Proceeding of International Conference on Artificial Intelligence (ICAI05), Las Vegas (2005)Google Scholar
  8. 8.
    Murphy, R., Assumes, M., Bugajsk, M., Johnson, T., Kelley, N., Kiefer, J., Pollock, L.: Marsupial-like mobile robot societies. In: Proceeding of Fourth International Conference on Autonomous Agents, pp. 364–365. ACM (1999)Google Scholar
  9. 9.
    Murphy, R.R.: Activities of the rescue robots at the world trade centre from 11–12 september 2001. Proc. IEEE Robot. Autom. Mag. 3, 851–864 (2004)Google Scholar
  10. 10.
    Saeedi, P., Sorensen, S.A.: An algorithmic approach to generate after-disaster test fields for search and rescue agents. In: Proceedings of International Conference of Word Congress Engineering(IAENG-WCE), London (2009)Google Scholar
  11. 11.
    Security, Homeland: National Institute of Standards & Technology – Urban Search and Rescue Robot Performance Standards (2008)Google Scholar
  12. 12.
    Svennebring, J., Koenig, S.: Building terrain-covering ant robots: a feasibility study. Auton. Robots 16(3), 313–332 (2004)CrossRefGoogle Scholar
  13. 13.
    Voshell, A.W.M., Woods, D.D.: Overcoming the keyhole in human–robot coordination: simulation and evaluation. In: Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting (2005)Google Scholar
  14. 14.
    Voss, R.F.: Fractals in nature: from characterization to simulation. The Science of Fractal Images, pp. 21–70, Springer-Verlag, New York, USA (1988)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity College LondonLondonUK

Personalised recommendations