1. Roland Geraerts and Mark Overmars CASA’08
Enhancing Corridor Maps for Real-Time Path Planning in Virtual Environments
Roland Geraerts and Mark Overmars
CASA’08

2. Criteria Fast and flexible path planner Natural paths
Real-time planning for thousands of characters
Dealing with local hazards
Natural paths
Smooth
Short
Keeps some distance to obstacles
Avoids other characters …

3. The CMM – Construction phase
The Corridor Map
A system of collision-free corridors for the static obstacles
Corridor: sequence of maximum clearance disks
Data structure: generalized VD + clearance + additional info
Corridor map
Corridor

4. The CMM – Construction phase
Computing the GVD
Draw distance mesh for each obstacle with GPU
Parallel projection of meshes
Trace boundaries
Prune the graph
Re-sampling
Increases efficiency
Adding data
Identify connected components
For each corridor, store maximum clearance a character can have

5. Experiments – Construction phase
McKenna MOUT environment
Footprint and Corridor Map: 0.05s

6. Experiments – Construction phase
City environment
Footprint and Corridor Map: 0.64s

7. The CMM – Query phase
Extract corridor for start and goal  global route
Character follows attraction point  local route
Runs along backbone path toward goal
Used to define a force function, applied to character
Obtain path
Integration over time, update velocity/position/attraction point
Yields a smooth (C1-continuous) path
Other behavior: locally adjust path by adding forces
Query points
Corridor+backbone
Path

8. The CMM – Query phase
For start/goal, find closest disk enclosing the character
kd-tree
Find the shortest backbone path
Dijkstra versus A*
Compute the corridor
Compute the path
Verlet integration
Query points
Corridor+backbone
Path

9. Experiments – Query phase
McKenna MOUT environment
Corridor and path: 0.2ms (average)

10. Experiments – Query phase
City environment
Corridor and path: 1.2ms (average)

11. Crowd Simulation Goal oriented behavior Obstacle avoidance
Each character has its own long term goal
A start and goal fixes a corridor
When a character has reached its goal, a new goal will be chosen
Obstacle avoidance
Helbing and Molnar’s social force model
Efficient nearest neighbor computations
2D grid storing the characters
Helbing and Molnar’s social force model; forces:
Acceleration toward the desired velocity of motion
Repulsive forces from other characters and borders to keep some clearance
Attractive forces among characters

12. Crowd Simulation – Experiments
Performance (1 cpu)

13. Crowd Simulation
Example

14. Conclusions The Corridor Map Method is fast
~10,000 characters can be simulated in real-time
The Corridor Map Method is flexible
Collision avoidance
Crowds
The Corridor Map Method produces natural paths
Smooth
Short
Keeps some distance to obstacles …