Efficient Multi-Agent Path Planning

  • Okan Arikan
  • Stephen Chenney
  • D. A. Forsyth
Part of the Eurographics book series (EUROGRAPH)


Animating goal-driven agents in an environment with obstacles is a time consuming process, particularly when the number of agents is large. In this paper, we introduce an efficient algorithm that creates path plans for objects that move between user defined goal points and avoids collisions. In addition, the system allows “culling” of some of the computation for invisible agents: agents are accurately simulated only if they are visible to the user while the invisible objects are approximated probabilistically. The approximations ensure that the agent’s behaviors match those that would occur had they been fully simulated, and result in significant speedups over running the accurate simulation for all agents.


path planning virtual agents proxy simulations simulation level of detail 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Barraquand and J. Latombe. A monte-carlo algorithm for path planning with many degrees of freedom. In IEEE Int. Conf. Robot. & Autom., pages 1712–1717, 1990.257,1990.Google Scholar
  2. 2.
    Michael Batty, Bin Jiang, and Mark Thurstain-Goodwin. Working paper 4: Local movement: Agent-based models of pedestrian flows. Working Paper from the Center for Advanced Spatial Analysis, University College London, 1998.Google Scholar
  3. 3.
    Christopher M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.Google Scholar
  4. 4.
    Stephen Chenney, Okan Arikan, and D.A. Forsyth. Proxy simulations for efficient dynamics. To appear in Eurographics 2001, Short Presentations.Google Scholar
  5. 5.
    B. Faverjon and P. Toumassoud. A local based approach for path planning of manipulators with a high number of degrees of freedom, int. conf. robotics & automation, 1987.Google Scholar
  6. 6.
    Guibas and Hershberger. Optimal shortest path queries in a simple polygon. In COMPGEOM: Annual ACM Symposium on Computational Geometry, 1987.Google Scholar
  7. 7.
    Demis Hassabis. Level-of-detail ai. Lecture at the 2001 Game Developers Conference.Google Scholar
  8. 8.
    Joseph O’Rouke Jacob E. Goodman. Discrete and Computational Geometry. The CRC Press, Boca Raton, New York, 1997.Google Scholar
  9. 9.
    Lydia Kavraki, Petr Svestka, Jean-Claude Latombe, and Mark Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 1996.Google Scholar
  10. 10.
    Jean-Paul Laumond. Robot Motion Planning and Control. Lectures Notes in Control and Information Sciences. Springer Verlag, 1998.CrossRefGoogle Scholar
  11. 11.
    J. Mitchell. Shortest paths and networks. In J. E. Goodman and J. O’Rourke, editors, Handbook of Discrete and Computational Geometry, CRC Press LLC, Boca Raton, FL, 1997.Google Scholar
  12. 12.
    L. Overgaard, H. Petersen, and J. Perram. Reactive motion planning: a multi-agent approach. Applied Artificial Intelligence, 10(1), 1996.Google Scholar
  13. 13.
    M. Overmars. A random approach to motion planning. Technical Report RUU-CS-92-32, Department of Computer Science, Utrecht University, The Netherlands, 1992.Google Scholar
  14. 14.
    S. N. Maheshwari Sanjiv Kapoor. Efficiently constructing the visibility graph of a simple polygon with obstacles. In SIAM Journal on Computing, volume 30(3), pages 847–871, August 2000.MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Dimitris Metaxas Siome Goldenstein, Edward Large. Special issue on real-time virtual worlds: Non-linear dynamical system approach to behavior modeling. In The Visual Computer, volume 15, pages 341–348, 1999.CrossRefGoogle Scholar
  16. 16.
    Marjolaine Tremblay and Hiromi Ono. Multiple creatures choreograhy on Star Wars: Episode I “The Phantom Menace”. SIGGRAPH 99 Animation Sketch. In Conference Abstracts and Applications, page 205, August 1999.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Okan Arikan
    • 1
  • Stephen Chenney
    • 2
  • D. A. Forsyth
    • 1
  1. 1.University of California at BerkeleyUSA
  2. 2.University of Wisconsin at MadisonUSA

Personalised recommendations