Optimal shortest path queries in a simple polygon
SCG '87 Proceedings of the third annual symposium on Computational geometry
Handbook of discrete and computational geometry
Multiple creatures choreography on Star Wars: (Episode I “The Phantom Menance”)
ACM SIGGRAPH 99 Conference abstracts and applications
Robot Motion Planning and Control
Robot Motion Planning and Control
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Efficiently Constructing the Visibility Graph of a Simple Polygon with Obstacles
SIAM Journal on Computing
Real-time Path Planning for Navigation in Unknown Environment
TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
Context-Aware Path Planning in Ubiquitous Network
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
A mathematical model, implementation and study of a swarm system
Robotics and Autonomous Systems
An environment-aware mobility model for wireless ad hoc network
Computer Networks: The International Journal of Computer and Telecommunications Networking
Path planning for complex 3D multilevel environments
Proceedings of the 24th Spring Conference on Computer Graphics
UbiPaPaGo: Context-aware path planning
Expert Systems with Applications: An International Journal
Mobility management in ubiquitous environments
Personal and Ubiquitous Computing
From geometry to spatial reasoning: automatic structuring of 3d virtual environments
MIG'11 Proceedings of the 4th international conference on Motion in Games
Generation and use of sparse navigation graphs for microscopic pedestrian simulation models
Advanced Engineering Informatics
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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.