HeuristicsA single heuristics definition
Table of content
The Azimuth heuristic favors traversing edges that are directed toward the search goal.
From a purely result perspective, it may look like a shortest path because it tend to produce more “straight” results going from seed to goal (if the topologies allows for it), but under the hood it’s a very different logic. This heuristics works best when combined with other more intricate ones to enforce some visual stability to the path.
Properties
| Property | Description |
|---|---|
| Basics | |
| Weight Factor | Weight of this heuristic against other concurrent heuristics. The higher the value, the more important it is during resolution. |
| Invert | Whether the score of this heuristic should be inverted. This effectively samples the score curve backwards. |
| Score Curve | Curve over which the heuristic values will be remapped. |
| Local Weight | |
| Use Local Weight Multiplier | If enabled, this heuristic will be using a dynamic, per-point weight factor. |
| Local Weight Multiplier Source | Whether to read the weight from Vtx or Edges points. |
| Local Weight Multiplier Attribute | Attribute to read the local weight from. |
| Roaming | |
| UVW Seed | Bounds-relative roaming seed point |
| UVW Goal | Bounds-relative roaming goal point |
Roaming seed/goal points are used as fallback in contexts that are using heuristics but don’t have explicit seed/goals; such as Cluster Refine‘MST or Score-based refinements.