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

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

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

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

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

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

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

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

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

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

Crowd Simulation Goal oriented behavior Obstacle avoidance 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 Helbing and Molnar’s social force model; forces: Acceleration toward the desired velocity of motion Repulsive forces from other characters and borders to keep some clearance Attractive forces among characters

Crowd Simulation – Experiments Performance (1 cpu)

Crowd Simulation Example

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 …