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

Crowds In A Polygon Soup Next-Gen Path Planning David Miles 3/23/2006. Automated Build Process. Voxelize Polygon Soup. Identify Walkable Surfaces. Simplify Surface Perimeter. Partition Into Convex Areas. Convex Partitioning. Convex Area Graph Complete. Holes in the Surfaces.

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

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  1. Crowds In A Polygon SoupNext-Gen Path PlanningDavid Miles3/23/2006

  2. Automated Build Process

  3. Voxelize Polygon Soup

  4. Identify Walkable Surfaces

  5. Simplify Surface Perimeter

  6. Partition Into Convex Areas

  7. Convex Partitioning

  8. Convex Area Graph Complete

  9. Holes in the Surfaces

  10. Splice Location

  11. Single Polygon Surface

  12. Convex Area Graph Complete

  13. Overhead Obstacles

  14. Walkable Surface Removal

  15. Changing WorldsDynamic Obstacles • Dynamic obstacles reconfigure world • Dynamic areas added • Pathfinding algorithm unchanged

  16. Boolean Subtract - =

  17. Boolean Subtract One Area

  18. Non-Convex Dynamic Areas

  19. Establish Connectivity

  20. Match Up Remaining Edges

  21. 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

  22. 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

  23. 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.

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

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

  26. Finding the Next Corner

  27. “Portals” Between Areas

  28. Initializing the Loop

  29. First Update, Left Side

  30. First Update Complete

  31. Second Update, Left Side

  32. Second Update Complete

  33. Third Update, Left Side

  34. Final Output

  35. “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

  36. 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

  37. Special Thanks • Meilin Wong • Hong Park and David Modiano • Crystal Dynamics • dmiles@babelflux.com • www.babelflux.com

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