use case: path finding, hydrology/flow fixing
Add a fast coarse-to-fine shortest path algorithm for large 2D grids, prioritizing speed over perfect optimality.
The idea is to:
- Solve the path on a coarse version of the cost map using Dijkstra.
- Upsample the resulting path to the next resolution.
- Build a narrow corridor around the path.
- Solve again at the finer resolution, restricted to this corridor.
- Repeat for a configurable number of levels.
This should drastically reduce the number of cells explored while retaining a good approximation of the full-resolution shortest path.
Possible parameters:
- number of levels
- corridor width / width decay
- A* vs Dijkstra
- optional path smoothing
The approach should work particularly well for large terrain cost maps where the optimal path is expected to vary smoothly across scales.
use case: path finding, hydrology/flow fixing
Add a fast coarse-to-fine shortest path algorithm for large 2D grids, prioritizing speed over perfect optimality.
The idea is to:
This should drastically reduce the number of cells explored while retaining a good approximation of the full-resolution shortest path.
Possible parameters:
The approach should work particularly well for large terrain cost maps where the optimal path is expected to vary smoothly across scales.