Crowds In A Polygon Soup NextGen Path Planning
- Slides: 51
Crowds In A Polygon Soup Next-Gen Path Planning David Miles 3/23/2006 Copyright 2006 Babel. Flux LLC
“Large-Scale Fluid AI Navigation in Complex, Changing Worlds” 1. Build a “usable free space” nav graph 2. Update graph for dynamic obstacles 3. Use graph for fluid navigation
1. Building the Nav-Graph Automated Build Process 1. Large, Complex Worlds 2. Polygon Soup Mesh 3. Multiple Unit Sizes
2. Dynamic Obstacle Updates 1. Obstacles reconfigure the world 2. Dynamic areas replace static areas 3. Underlying algorithms work exactly as before
3. Fluid AI Navigation 1. 2. 3. 4. Fast ray casts Edge detection Potential Fields Path Following
Automated Build Process
1. Voxelize Polygon Soup
2. Extract Edge
3. Simplify Polygon
4. Partition Into Convex Areas
Convex Partitioning
Recursively Partition
Convex Area Graph Complete
Pathfinding
Holes in the Free-Space
Splice Location
Add Splice Segments
Single Polygon Surface
Convex Area Graph Complete
Overhead Obstacles
Walkable Surface Removal
3 d overlap
3 d Partitioning
3 d Partitioning After initial split, overlap no longer exists
Different Creature Shapes
Different Creature Shapes
Non-Uniform Voxelization
Part 2: Dynamic Obstacles • Dynamic obstacles reconfigure world • Underlying algorithms unchanged
Boolean Subtract - =
Partition Non-Convex Areas
Multiple Obstacles
Memory Layout
Part 3: Fluid AI Navigation What can I do with my convex area graph? – Very fast navigational ray-casts – Distance to edge queries – Repulsion fields on edges – Smart path following
Ray Casts
Smart Path Following Desired flocking behavior Common approach attracts each flock member to the minimum distance path. Does not adapt to available freespace. Repulsion from edges can easily trap individuals in local minima.
For The Pathing Connoisseur Unneeded return to the original path annoys the pathing connoisseur Ahhh, much better
Next Corner Each member of the flock steers left or right to head towards the “next corner”.
Finding the Next Corner
“Portals” Between Areas
Initializing the Loop
First Iteration, Left Side
First Iteration, Right Side
First Iteration Complete
Second Iteration, Left Side
Second Iteration, Right Side
Second Iteration Complete
Third Iteration, Left Side
Final Output
“Next Corner” Summary • • • Easy to compute from convex graph < 2 cross-products per iteration Can limit iterations Never explicitly create min-dist path Fluid path following even with large disturbances
Conclusions • Automated build of convex area graph – efficiently represents the usable free space – operates on polygon soup mesh – settable enemy size and shape • Dynamic obstacles update graph – no speed overhead once updated • Fluid AI navigation using the graph – Many useful queries performed rapidly
Special Thanks • Meilin Wong • Hong Park and David Modiano • Crystal Dynamics • dmiles@navpower. com • www. navpower. com
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