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Efficient Corridor Maps for Real-Time Path Planning

Fast path planner for thousands of characters, avoiding hazards, smooth and short paths while keeping distance from obstacles. Construction phase, GVD computation, and query phase explained. Achieving smooth, efficient, and collision-free paths. Crowd simulation scenarios included.

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Efficient Corridor Maps for Real-Time Path Planning

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  1. Enhancing Corridor Maps for Real-Time Path Planning in Virtual Environments Roland Geraerts and Mark Overmars CASA’08

  2. Criteria • Fast and flexible path planner • Real-time planning for thousands of characters • Dealing with local hazards • Natural paths • Smooth • Short • Keeps some distance to obstacles • Avoids other characters • …

  3. 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

  4. 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

  5. Experiments – Construction phase McKenna MOUT environment Footprint and Corridor Map: 0.05s

  6. Experiments – Construction phase City environment Footprint and Corridor Map: 0.64s

  7. 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

  8. 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

  9. Experiments – Query phase McKenna MOUT environment Corridor and path: 0.2ms (average)

  10. Experiments – Query phase City environment Corridor and path: 1.2ms (average)

  11. 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

  12. Crowd Simulation – Experiments • Performance (1 cpu)

  13. Crowd Simulation • Example

  14. 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 • Smooth • Short • Keeps some distance to obstacles • …

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