Roadmap-Based Level Clearing of Buildings

  • Samuel Rodriguez
  • Nancy M. Amato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7060)

Abstract

In this paper we describe a roadmap-based approach for a multi-agent search strategy to clear a building or multi-story environment. This approach utilizes an encoding of the environment in the form of a graph (roadmap) that is used to encode feasible paths through the environment. The roadmap is partitioned into regions, e.g., one per level, and we design region-based search strategies to cover and clear the environment. We can provide certain guarantees within this roadmap-based framework on coverage and the number of agents needed. Our approach can handle complex and realistic environments where many approaches are restricted to simple 2D environments.

Keywords

Goal Location Coverage Location Clearing Path Pathway Assignment Geometric Coverage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C.V., Vallejo, D.: OBPRM: An obstacle-based PRM for 3D workspaces. In: Robotics: The Algorithmic Perspective, pp. 155–168. A.K. Peters, Natick (1998); Proc. Third Workshop on Algorithmic Foundations of Robotics (WAFR), Houston, TX (1998) Google Scholar
  2. 2.
    Amit, Y., Mitchell, J.S.B., Packer, E.: Locating guards for visibility coverage of polygons. In: Proceedings of the Workshop on Algorithm Engineering and Experiments, ALENEX (2007)Google Scholar
  3. 3.
    Bayazit, O., Lien, J.M., Amato, N.M.: Better group behaviors in complex environments using global roadmaps. In: Artif. Life, pp. 362–370 (December 2002)Google Scholar
  4. 4.
    Bayazit, O., Lien, J., Amato, N.M.: Roadmap-based flocking for complex environments. In: Proc. Pacific Graphics, pp. 104–113 (October 2002)Google Scholar
  5. 5.
    Bopardikar, S., Bullo, F., Hespanha, J.: Cooperative pursuit with sensing limitations. In: American Control Conference (ACC), pp. 935–953 (July 2007)Google Scholar
  6. 6.
    Burgard, W., Moors, M., Fox, D., Simmons, R., Thrun, S.: Collaborative multi-robot exploration. In: Proc. IEEE Int. Conf. Robot. Autom (ICRA), pp. 476–481 (2000)Google Scholar
  7. 7.
    Durham, J.W., Franchi, A., Bullo, F.: Distributed pursuit-evasion with limited-visibility sensors via frontier-based exploration. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 3562–3568 (2010)Google Scholar
  8. 8.
    Gerkey, B.P., Thrun, S., Gordon, G.: Visibility-based pursuit-evasion with limited field of view. Int. J. Robot. Res. 25(4), 299–315 (2006)CrossRefGoogle Scholar
  9. 9.
    Guibas, L.J., claude Latombe, J., Lavalle, S.M., Lin, D., Motwani, R.: Visibility-based pursuit-evasion in a polygonal environment. International Journal of Computational Geometry and Applications, 17–30 (1997)Google Scholar
  10. 10.
    Isler, V., Kannan, S., Khanna, S.: Randomized pursuit-evasion with limited visibility. In: Proc. ACM-SIAM Symposium on Discrete Algorithms, pp. 1060–1069 (2004)Google Scholar
  11. 11.
    Isler, V., Sun, D., Sastry, S.: Roadmap based pursuit-evasion and collision avoidance. In: Proc. Robotics: Sci. Sys., RSS (2005)Google Scholar
  12. 12.
    Kavraki, L.E., Švestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Automat. 12(4), 566–580 (1996)CrossRefGoogle Scholar
  13. 13.
    Kolling, A., Kleiner, A., Lewis, M., Sycara, K.: Solving pursuit-evasion problems on height maps. In: IEEE International Conference on Robotics and Automation (ICRA 2010) Workshop: Search and Pursuit/Evasion in the Physical World: Efficiency, Scalability, and Guarantees (2010)Google Scholar
  14. 14.
    Kolling, A., Carpin, S.: Cooperative observation of multiple moving targets: an algorithm and its formalization. Int. J. Rob. Res. 26, 935–953 (2007)CrossRefGoogle Scholar
  15. 15.
    Kolling, A., Carpin, S.: Multi robot pursuit evasion without maps. In: Proc. IEEE Int. Conf. Robot. Autom (ICRA), pp. 3045–3051 (May 2010)Google Scholar
  16. 16.
    Kolling, A., Carpin, S.: Pursuit-evasion on trees by robot teams. Trans. Rob. 26(1), 32–47 (2010)CrossRefGoogle Scholar
  17. 17.
    Lavalle, S.M., Lin, D., Guibas, L.J., Claude Latombe, J., Motwani, R.: Finding an unpredictable target in a workspace with obstacles. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 737–742 (1997)Google Scholar
  18. 18.
    O’Rourke, J.: Art Gallery Theorems and Algorithms. Oxford University Press, New York (1987)MATHGoogle Scholar
  19. 19.
    Parsons, T.D.: Pursuit-evasion in a graph. In: Theory and Applications of Graphs. Lecture Notes in Mathematics, vol. 642, pp. 426–441. Springer, Heidelberg (1978)CrossRefGoogle Scholar
  20. 20.
    Rodriguez, S., Denny, J., Mahadevan, A., Vu, J., Burgos, J., Zourntos, T., Amato, N.M.: Roadmap-based pursuit-evasion in 3d environments. Transactions on Edutainment (to appear, 2011)Google Scholar
  21. 21.
    Rodriguez, S., Denny, J., Zourntos, T., Amato, N.M.: Toward Simulating Realistic Pursuit-Evasion Using a Roadmap-Based Approach. In: Boulic, R., Chrysanthou, Y., Komura, T. (eds.) MIG 2010. LNCS, vol. 6459, pp. 82–93. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Wilmarth, S.A., Amato, N.M., Stiller, P.F.: MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), vol. 2, pp. 1024–1031 (1999)Google Scholar
  23. 23.
    Yamauchi, B.: Frontier-based exploration using multiple robots. In: International Conference on Autonomous Agents (Agents 1998), pp. 47–53 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Samuel Rodriguez
    • 1
  • Nancy M. Amato
    • 1
  1. 1.Parasol Lab, Dept. Computer Science and EngineeringTexas A&M UniversityUSA

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