Advertisement

Covering a Continuous Domain by Distributed, Limited Robots

  • Eliyahu Osherovich
  • Alfred M. Bruckstein
  • Vladimir Yanovski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

We present an algorithm for covering continuous domains by primitive robots whose only ability is to mark visited places with pheromone and to sense the level of the pheromone in their neighborhood. These pheromone marks can be sensed by all robots and thus provide a way for indirect communication between the robots. Apart from this, the robots have no means to communicate. Additionally they are memoryless, have no global information such as the domain map, own position, coverage percentage, etc. Despite the robots’ simplicity, we show that they are able to cover efficiently any connected domains, including non-planar ones.

Keywords

Time Instance Cover Time Continuous Domain Noisy Pixel Indirect Communication 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yanovski, V., Wagner, I.A., Bruckstein, A.M.: Vertex-ant-walk - A robust method for efficient exploration of faulty graphs. Annals of Mathematics and Artificial Intelligence 31(1-4), 99–112 (2001)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Bruckstein, A.M.: Why the ant trails look so straight and nice. The Mathematical Intelligencer 15(2), 58–62 (1993)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Hölldobler, B., Wilson, E.O.: The Ants! Harvard University Press, Cambridge (1990)Google Scholar
  4. 4.
    Schöne, H.: Spatial orientation: the spatial control of behavior in animals and man. Princeton University Press, Princeton (1984)Google Scholar
  5. 5.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: Optimization by a colony of cooperating agents. IEEE Trans. on Systems, Man, and Cybernetics–Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  7. 7.
    Wagner, I.A., Bruckstein, A.M.: From ants to a(ge)nts: A special issue on ant-robotics. Annals of Mathematics and Artificial Intelligence 31(1-4), 1–5 (2001)CrossRefGoogle Scholar
  8. 8.
    Bonabeau, E., Théraulaz, G.: Swarm smarts. Scientific American 282(3), 72–79 (2000)CrossRefGoogle Scholar
  9. 9.
    Russell, R.A.: Ant trails - an example for robots to follow? In: ICRA, pp. 2698–2703 (1999)Google Scholar
  10. 10.
    Koenig, S., Liu, Y.: Terrain coverage with ant robots: a simulation study. In: AGENTS 2001: Proceedings of the fifth international conference on Autonomous agents, pp. 600–607. ACM Press, New York (2001)CrossRefGoogle Scholar
  11. 11.
    Blum, M., Sakoda, W.: On the capability of finite automata in 2 and 3 dimensional space. In: Ann. Symp. on Foundations in Computer Science, pp. 147–161 (1977)Google Scholar
  12. 12.
    Blum, M., Kozen, D.: On the power of the compass. In: Proc. 19th Ann. Symp. on Foundations in Computer Science, pp. 132–142 (1978)Google Scholar
  13. 13.
    Bender, M.A., Fernández, A., Ron, D., Sahai, A., Vadhan, S.: The power of a pebble: exploring and mapping directed graphs. In: STOC 1998: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 269–278. ACM Press, New York (1998)CrossRefGoogle Scholar
  14. 14.
    Wagner, I.A., Lindenbaum, M., Bruckstein, A.M.: Smell as a computational resource — A lesson we can learn from the ant. In: Proceedings of the 4th Israel Symposium on Theory of Computing and Systems, ISTCS 1996, Jerusalem, Israel, June 10-12, pp. 219–230. IEEE Computer Society Press, Los Alamitos-Washington-Brussels-Tokyo (1996)Google Scholar
  15. 15.
    Wagner, I.A., Lindenbaum, M., Bruckstein, A.M.: Efficiently searching a graph by a smell-oriented vertex process. Annals of Mathematics and Artificial Intelligence 24(1-4), 211–223 (1998)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research 5(1), 90–98 (1986)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Zelinsky, A., Byrne, J.C., Jarvis, R.A.: Planning paths of complete coverage of an unstructured environment by a mobile robot. In: International Conference on Advanced Robotics (ICAR) (1993)Google Scholar
  18. 18.
    Choset, H., Pignon, P.: Coverage path planning: The boustrophedon decomposition. In: International Conference on Field and Service Robotics (1997)Google Scholar
  19. 19.
    Butler, Z.J.: Distributed coverage of rectilinear environments. PhD thesis, Carnegie Mellon University (2000)Google Scholar
  20. 20.
    Acar, E.U., Choset, H., Zhang, Y., Schervish, M.J.: Path planning for robotic demining: Robust sensor-based coverage of unstructured environments and probabilistic methods. I. J. Robotic Res. 22(7-8), 441–466 (2003)CrossRefGoogle Scholar
  21. 21.
    Wagner, Lindenbaum, Bruckstein: MAC vs. PC: Determinism and randomness as complementary approaches to robotic exploration of continuous unknown domains. ROBRES: The International Journal of Robotics Research 19 (2000)Google Scholar
  22. 22.
    Gage, D.W.: Randomized search strategies with imperfect sensors. In: Chun, W.H., Wolfe, W.J. (eds.) Proc. SPIE, vol. 2058, pp. 270–279 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eliyahu Osherovich
    • 1
  • Alfred M. Bruckstein
    • 1
  • Vladimir Yanovski
    • 1
  1. 1.Computer Science DepartmentTechnion – Israel Institute of TechnologyHaifaIsrael

Personalised recommendations