Transcript Enhancing Corridor Maps for Real

Enhancing Corridor Maps for Real-Time
Path Planning in Virtual Environments
Roland Geraerts and Mark Overmars
CASA’08
Criteria
Fast and flexible path planner
• Real-time planning for thousands of characters
• Dealing with local hazards
Natural paths
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Smooth
Short
Keeps some distance to obstacles
Avoids other characters
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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
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
Experiments – Construction phase
McKenna MOUT environment
Footprint and Corridor Map: 0.05s
Experiments – Construction phase
City environment
Footprint and Corridor Map: 0.64s
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
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
Experiments – Query phase
McKenna MOUT environment
Corridor and path: 0.2ms (average)
Experiments – Query phase
City environment
Corridor and path: 1.2ms (average)
Crowd Simulation
Goal oriented behavior
• 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
Crowd Simulation – Experiments
Performance (1 cpu)
Crowd Simulation
Example
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
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Smooth
Short
Keeps some distance to obstacles
…