Pathfinding Over Streaming Terrain By James Stewart jmstewarsmu

Pathfinding Over Streaming Terrain By James Stewart jmstewar@smu. edu

The Problem: How do you find a path when the search space is too large to fit into memory?

Features of the Solution • Use same data representation for both rendering and AI. • Solution requires no explicit intervention by a designer—terrain is by definition very large. • Handles only static pathfinding. Dynamic obstacles such as deformable terrain or physics entities require a separate AI layer.

Terrain Preprocessing • A simple tool packages a gigantic height map into leaf nodes of a Restricted Quadtree Triangulation. • (Long story short, Wouter’s LOD method plus stripification and an error metric to collapse distant vertices. )

Terrain Preprocessing • Lowest LOD used as a basis for a navmesh. • Export neighbor accessibility summary (eight neighbors = 3 bits / chunk)

Terrain Database 1. Dedicated thread asynchronously pages chunks from storage to be accessed as memory-mapped files. 2. Paging candidates and victims selected via motion prediction (prediction scheme very dependent on game design).

AI Solution • Use neighbor accessibility classification to build a “good enough” path. • Refine path over subsequent frames by paging in chunks along path.

Challenges • Scalability: How will database prioritize paging requests when many units each require different chunks, and memory is limited? • Path Optimality: Is the “good enough” path really good enough? • Compression: How can the size of the terrain and navmesh data be reduced?