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 EnvironmentSkeleton of the map Corridor

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 environment 2D 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

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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

• http://people.cs.uu.nl/ioannis/irm