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

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

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




5 Automated Build Process

6 Voxelize Polygon Soup

7 Identify Walkable Surfaces

8 Simplify Surface Perimeter

9 Partition Into Convex Areas

10 Convex Partitioning

11 Convex Area Graph Complete

12 Holes in the Surfaces

13 Splice Location

14 Single Polygon Surface

15 Convex Area Graph Complete

16 Overhead Obstacles

17 Walkable Surface Removal

18 Changing Worlds Dynamic Obstacles Dynamic obstacles reconfigure world Dynamic areas added Pathfinding algorithm unchanged

19 Boolean Subtract - =

20 Boolean Subtract One Area

21 Non-Convex Dynamic Areas

22 Establish Connectivity

23 Match Up Remaining Edges

24 Dynamic Obstacle Summary Find areas affected by dynamic obstacle Record any edges entering from outside Boolean subtract Partition any non-convex dynamic areas Match up unconnected edges

25 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 –“Next corner” determination

26 Path Following in Flocking 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.

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

28 It’s Easy Each member of the flock steers left or right to head towards the “next corner”.

29 Finding the Next Corner

30 “Portals” Between Areas

31 Initializing the Loop

32 First Update, Left Side

33 First Update Complete

34 Second Update, Left Side

35 Second Update Complete

36 Third Update, Left Side

37 Final Output

38 “Next Corner” Summary Easy to compute from convex graph < 2 cross-products per iteration Can limit iterations Never explicitly create min-dist path Fluid motion even with large disturbances

39 Conclusions Automated build of convex area graph –efficiently represents the walkable 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

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

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