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Crowds In A Polygon Soup Next-Gen Path Planning David Miles 3/23/2006 Copyright 2006 BabelFlux LLC.

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Presentation on theme: "Crowds In A Polygon Soup Next-Gen Path Planning David Miles 3/23/2006 Copyright 2006 BabelFlux LLC."— Presentation transcript:

1 Crowds In A Polygon Soup Next-Gen Path Planning David Miles 3/23/2006 Copyright 2006 BabelFlux LLC

2 “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

3 1. Building the Nav-Graph Automated Build Process 1.Large, Complex Worlds 2.Polygon Soup Mesh 3.Multiple Unit Sizes

4 2. Dynamic Obstacle Updates 1.Obstacles reconfigure the world 2.Dynamic areas replace static areas 3.Underlying algorithms work exactly as before

5 1.Fast ray casts 2.Edge detection 3.Potential Fields 4.Path Following 3. Fluid AI Navigation

6 Automated Build Process

7 1. Voxelize Polygon Soup

8 2. Extract Edge

9 3. Simplify Polygon

10 4. Partition Into Convex Areas

11 Convex Partitioning

12 Recursively Partition

13 Convex Area Graph Complete

14 Pathfinding

15 Holes in the Free-Space

16 Splice Location

17 Add Splice Segments

18 Single Polygon Surface

19 Convex Area Graph Complete

20 Overhead Obstacles

21 Walkable Surface Removal

22 3d overlap

23 3d Partitioning

24 After initial split, overlap no longer exists

25 Different Creature Shapes

26

27 Non-Uniform Voxelization

28 Part2: Dynamic Obstacles Dynamic obstacles reconfigure world Underlying algorithms unchanged

29 Boolean Subtract - =

30 Partition Non-Convex Areas

31 Multiple Obstacles

32 Memory Layout

33 Part3: 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

34 Ray Casts

35 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.

36 For The Pathing Connoisseur Unneeded return to the original path annoys the pathing connoisseur Ahhh, much better

37 Next Corner Each member of the flock steers left or right to head towards the “next corner”.

38 Finding the Next Corner

39 “Portals” Between Areas

40 Initializing the Loop

41 First Iteration, Left Side

42 First Iteration, Right Side

43 First Iteration Complete

44 Second Iteration, Left Side

45 Second Iteration, Right Side

46 Second Iteration Complete

47 Third Iteration, Left Side

48 Final Output

49 “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

50 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

51 Special Thanks Meilin Wong Hong Park and David Modiano Crystal Dynamics


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