Ioannis Karamouzas, Roland Geraerts, Mark Overmars Indicative Routes for Path Planning and Crowd Simulation

Path Planning What is path planning Steer character from A to B Computer games and path planning Fast and flexible – Real-time planning for thousands of characters – Flexibility to avoid other characters and local hazards – Individuals and groups Natural Paths – Smooth – Collision-free – Short – Keep a preferred amount of clearance from obstacles – Flexible – …..

Path Planning Maintain suspense of disbelief Realistic graphics and physics Still though, the path choices that characters make are poor

Errors in Path Planning

Existing Algorithms Grid-based A* Algorithms Computational expensive Aesthetically unpleasant paths Waypoint graphs Hand designed Do not adapt to changes in the environment Navigation Meshes Automatic construction is slow Paths need to be smoothed

Existing Algorithms Local Methods Flocking [Reynolds, 1987 & 1999], Helbing’s Social Force Model [Helbing et al, 2000] Reactive style planners Local methods fail to find a route Suffer from local minima problems Lead to repeated motion

The Indicative Route Method In real life, people Do not plan an exact path, but A preferred/desired global route A path planning algorithm should produce: An Indicative Route – Guides character to its goal A corridor – Allows for flexibility

The Indicative Route Method The IRM method in action: A collision-free indicative route determines the character’s preferred route A corridor around this route defines the walkable area for the character A smooth path is generated using a force-field approach

Computing Corridors The Corridor Map Introduced by [Geraerts and Overmars, 2007] Provides a system of collision-free corridors Corridor: sequence of maximum clearance disks The Corridor Map is computed as follows: The Generalized Voronoi Diagram is approximated using GPU [Hoff et al, 1999] Clearance + additional info is stored 3D Environment Skeleton of the mapCorridor

Computing Corridors Computing the Corridor Map Only required during preprocessing Very fast (50 ms, NVIDIA GeForce 8800 GTX ) Compute a corridor Retract the indicative route to the Generalized Voronoi Diagram Find corresponding path in diagram Use clearance information as a representation of the corridor

Local Navigation in IRM Boundary force Find closest point on corridor boundary Increases to infinity when close to boundary 0 when clearance is large enough (or when on GVD) Steering force An attraction point moves along the indicative route Attracts the character with a constant steering force Noise force Create variation in paths Perlin noise is used

Local Navigation in IRM Collision Avoidance Force Avoid collision with other characters and moving entities Helbing’s model can be used Additional models can be easily incorporated Obtain the final path Force leads to an acceleration term Integration over time, update velocity/position/attraction point Results in a smooth (C1-continuous) path

IRM method Resulting vector field Indicative Route is the medial axis

Creating Indicative Routes Use the Generalized Voronoi Diagram Retract start and goal Find shortest path (using A*) The corridor is obtained immediately Use a network of Indicative Routes Created by level designer Voronoi-Visibility Complex [Wein et al, 2005] A* on coarse grid Additional information can be incorporated – For example flow into account – Use the notion of Influence Regions

Crowd Simulation Method can plan paths of a large number of characters Goal oriented behavior – Each character has its own long term goal – When a character reaches its goal, a new goal is chosen Wandering behavior – Attraction points do a random walk on the indicative network Experiments Goal-oriented behavior Simulation ran for 1000 steps Each step calculates 0.1s of simulation time

Crowd Simulation - Experiments Test Environment City environment2D footprint (640 ms)

Crowd Simulation - Experiments Performance 2.4 GHz Intel Core2 Duo, 2 GB memory One CPU core used 3000 characters, CPU usage 26%, FPS 33

Crowd Simulation – Video

Current Research Global behavior Incorporating influence regions Types of behavior (shopping, tourists, …) Further improving the local methods Take mood and personality into account Dealing with small groups Observing and modeling paths of real humans Motion capture data Tracking pedestrians Evaluation of the results Project’s Website