Advertisement

Beyond Frontier Exploration

  • Arnoud Visser
  • Xingrui-Ji
  • Merlijn van Ittersum
  • Luis A. González Jaime
  • Laurenţiu A. Stancu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5001)

Abstract

This article investigates the prerequisites for a global exploration strategy in an unknown environment on a virtual disaster site. Assume that a robot equipped with a laser range scanner can build a detailed map of a previous unknown environment. The remaining question is how to use this information on this map for further exploration.

On a map several interesting locations can be present where the exploration can be continued, referred as exploration frontiers. Typically, a greedy algorithm is used for the decision which frontier to explore next. Such a greedy algorithm only considers interesting locations locally, focused to reduce the movement costs. More sophisticated algorithms also take into account the information that can be gained along each frontier. This shifts the problem to estimate the amount of unexplored area behind the frontiers on the global map. Our algorithm exploits the long range of current laser scanners. Typically, during the previous exploration a small number of laser rays already passed the frontier, but this number is too low to have major impact on the generated map. Yet, the few rays through a frontier can be used to estimate the potential information gain from unexplored area beyond the frontier.

Keywords

Mobile Robot Observation Point Exploration Action Safe Region Occupancy Grid 
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.

References

  1. 1.
    Jacoff, A., Messina, E., Weiss, B., Tadokoro, S., Nakagawa, Y.: Test arenas and performance metrics for urban search and rescue robots. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (2003)Google Scholar
  2. 2.
    Balakirsky, S., Scrapper, C., Carpin, S., Lewis, M.: Usarsim: providing a framework for multi-robot performance evaluation. In: Proceedings of PerMIS 2006 (2006)Google Scholar
  3. 3.
    Balakirsky, S., Carpin, S., Kleiner, A., Lewis, M., Visser, A., Wang, J., Ziparo, V.A.: Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from robocup rescue. Journal of Field Robotics (to appear, 2007)Google Scholar
  4. 4.
    Hasegawa, B.R.: Continues observation planning for autonomous exploration. Master’s thesis, Massachusetts Institute of Technology (2004)Google Scholar
  5. 5.
    Pfingsthorn, M., Slamet, B., Visser, A., Vlassis, N.: Uva rescue team 2006 robocup rescue - simulation league. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006: Robot Soccer World Cup X. LNCS (LNAI), vol. 4434, Springer, Heidelberg (2007)Google Scholar
  6. 6.
    Pfingsthorn, M., Slamet, B., Visser, A.: A scalable hybrid multi-robot slam method for highly detailed maps. In: Robocup 2007: Robot Soccer World Cup XI. Lecture Notes on Artificial Intelligence (to be published, 2007)Google Scholar
  7. 7.
    Howard, A., Sukhatme, G.S., Matarić, M.J.: Multi-robot mapping using manifold representations. In: Proceedings of the IEEE - Special Issue on Multi-robot Systems (2006)Google Scholar
  8. 8.
    Pfister, S.T., Kriechbaum, K.L., Roumeliotis, S.I., Burdick, J.W.: A weighted range sensor matching algorithm for mobile robot displacement estimation. IEEE Transactions on Robotics and Automation (to appear, 2007)Google Scholar
  9. 9.
    Bourgault, F., Göktoǧan, A., Furukawa, T., Durrant-Whyte, H.F.: Coordinated search for a lost target in a bayesian world. Advanced Robotics 18(10), 979–1000 (2004)CrossRefGoogle Scholar
  10. 10.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press (2005)Google Scholar
  11. 11.
    Slamet, B., Pfingsthorn, M.: Manifoldslam: a multi-agent simultaneous localization and mapping system for the robocup rescue virtual robots competition. Master’s thesis, Universiteit van Amsterdam (2006)Google Scholar
  12. 12.
    Moravec, H.: Sensor fusion in certainty grids for mobile robots. AI Magazine 9, 61–74 (1988)Google Scholar
  13. 13.
    Fox, D., Burgard, W., Thrun, S.: Active markov localization for mobile robots. Robotics and Autonomous Systems 25, 195–207 (1998)CrossRefGoogle Scholar
  14. 14.
    Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation - mobile robot navigation with uncertainty in dynamic environments. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 34–40 (1999)Google Scholar
  15. 15.
    Simmons, R.G., Apfelbaum, D., Burgard, W., Fox, D., Moors, M., Thrun, S., Younes, H.: Coordination for multi-robot exploration and mapping. In: AAAI/IAAI, pp. 852–858 (2000)Google Scholar
  16. 16.
    Sim, R., Roy, N.: Global a-optimal robot exploration in slam. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain (2005)Google Scholar
  17. 17.
    Yamauchi, B.: A frontier based approach for autonomous exploration. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, Monterey, July 10-11, 1997 (1997)Google Scholar
  18. 18.
    González-Baños, H.H., Latombe, J.C.: Navigation Strategies for Exploring Indoor Environments. The International Journal of Robotics Research 21(10-11), 829–848 (2002)CrossRefGoogle Scholar
  19. 19.
    van Ittersum, M., Xingrui-Ji, Gonzalez, L., Stancu, L.: Natural boundaries. Report, Universiteit van Amsterdam (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Arnoud Visser
    • 1
  • Xingrui-Ji
    • 1
  • Merlijn van Ittersum
    • 1
  • Luis A. González Jaime
    • 1
  • Laurenţiu A. Stancu
    • 1
  1. 1.Intelligent Systems Laboratory AmsterdamUniversiteit van AmsterdamThe Netherlands

Personalised recommendations