Abstract
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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
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.
Christopher M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
Stephen Chenney, Okan Arikan, and D.A. Forsyth. Proxy simulations for efficient dynamics. To appear in Eurographics 2001, Short Presentations.
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.
Guibas and Hershberger. Optimal shortest path queries in a simple polygon. In COMPGEOM: Annual ACM Symposium on Computational Geometry, 1987.
Demis Hassabis. Level-of-detail ai. Lecture at the 2001 Game Developers Conference.
Joseph O’Rouke Jacob E. Goodman. Discrete and Computational Geometry. The CRC Press, Boca Raton, New York, 1997.
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.
Jean-Paul Laumond. Robot Motion Planning and Control. Lectures Notes in Control and Information Sciences. Springer Verlag, 1998.
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.
L. Overgaard, H. Petersen, and J. Perram. Reactive motion planning: a multi-agent approach. Applied Artificial Intelligence, 10(1), 1996.
M. Overmars. A random approach to motion planning. Technical Report RUU-CS-92-32, Department of Computer Science, Utrecht University, The Netherlands, 1992.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Wien
About this paper
Cite this paper
Arikan, O., Chenney, S., Forsyth, D.A. (2001). Efficient Multi-Agent Path Planning. In: Magnenat-Thalmann, N., Thalmann, D. (eds) Computer Animation and Simulation 2001. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6240-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6240-8_14
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83711-5
Online ISBN: 978-3-7091-6240-8
eBook Packages: Springer Book Archive