Dispersing robots in an unknown environment

  • Ryan Morlok
  • Maria Gini


We examine how the choice of the movement algorithm can affect the success of a swarm of simple mobile robots attempting to disperse themselves in an unknown environment. We assume there is no central control, and the robots have limited processing power, simple sensors, and no active communication. We evaluate different movement algorithms based on the percentage of the environment that the group of robots succeeds in observing.


Obstacle Avoidance Sensor Range Unknown Environment Artificial Potential Field Movement Algorithm 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, 1999.Google Scholar
  2. 2.
    H. Choset. Coverage for robotics — a survey of recent results. Annals of Mathematics and Artificial Intelligence, 31:113–126, 2001.CrossRefGoogle Scholar
  3. 3.
    B. P. Gerkey, R. T. Vaughan, K. Stöy, A. Howard, G. S. Sukhatme, and M. J. Matarić. Most valuable player: A robot device server for distributed control. In Proc. IEEE/RSJ InV’l Conf. on Intelligent Robots and Systems, pages 1226–1231, Oct. 2001.Google Scholar
  4. 4.
    T.-R. Hsiang, E. Arkin, M. A. Bender, S. Fekete, and J. Mitchell. Algorithms for rapidly dispersing robot swarms in unknown environments. In Proc. 5th Workshop on Algorithmic Foundations of Robotics (WAFR), 2002.Google Scholar
  5. 5.
    T.-R. Hsiang, E. Arkin, M. A. Bender, S. Fekete, and J. Mitchell. Online dispersion algorithms for swarms of robots. In Proc. of the 19th Annual ACM Symposium on Computational Geometry (SoCG), pages 382–383, 2003.Google Scholar
  6. 6.
    S. Koenig, B. Szymanski, and Y. Liu. Efficient and inefficient ant coverage methods. Annals of Mathematics and Artificial Intelligence, 31:41–76, 2001.CrossRefGoogle Scholar
  7. 7.
    J. C. Latombe. Robot Motion Planning. Kluwer Academic Publ., Norwell, MA, 1991.Google Scholar
  8. 8.
    D. Payton, M. Daily, R. Estkowski, M. Howard, and C. Lee. Pheromone robotics. Autonomous Robots, 11(3):319–324, Nov 2001.Google Scholar
  9. 9.
    P. E. Rybski, S. A. Stoeter, M. Gini, D. F. Hougen, and N. Papanikolopoulos. Performance of a distributed robotic system using shared communications channels. IEEE Trans, on Robotics and Automation, 22(5):713–727, Oct. 2002.Google Scholar
  10. 10.
    R. T. Vaughan. Stage: A multiple robot simulator. Technical Report IRIS-00-394, Institute for Robotics and Intelligent Systems, University of Southern California, 2000.Google Scholar
  11. 11.
    I. A. Wagner, M. Lindenbaum, and A. M. Bruckstein. MAC vs PC — determinism and randomness as complementary approaches to robotic exploration of continuous unknown domains. Int’l Journal of Robotics Research, 19(1):12–31, 2000.CrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Ryan Morlok
    • 1
  • Maria Gini
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolis

Personalised recommendations