VtxEdges
SeedsGoals
HeuristicsPathsRelated
PathfindingTable of content
The Edges Pathfinding node connects goal points to seed points. Contrary to the 
Plot Edges Pathfinding, this node uses an external module (Goal Picker) to match seeds and goal, and cannot generate closed paths; it only supports A to B (many of them as well).
Properties
| Property | Description |
|---|---|
| Goal Picker | The module that will be used to interpret and manipulate goals and seeds inputs. Each module has individual settings and documentation – See Available Goal Pickers. |
| Property | Description |
|---|---|
| Plot inclusiong | |
| Add Seed to Path | Prepends the seed position at the beginning of the output path. This will create a point with the position of the seed. |
| Add Goal to Path | Appends the goal position at the end of the output path. This will create a point with the position of the goal. |
| Path Composition | Whether the output paths are made from Vtx or Edges points. |
| Picking | |
| Seed Picking | Lets you control how the seed node (Vtx) will be picked based on the provided seed position. |
| Goal Picking | Lets you control how the goal node (Vtx) will be picked based on the provided goal position. |
| Search Algorithm | Let you pick which  ⊚ Search algorithm to use to resolve pathfinding. |
Modules
Available 
🝓 Goal Pickers modules
🝓 Goal from Attribute
Match seed with goals picked at an attribute-specified index.
The Goal from Attribute picker …
Available 
🝰 Heuristics modules
🝰 Heuristic Attribute
Attribute-driven heuristics
The Attribute heuristics uses custom point or edge value as raw score.
🝰 Feedback
Favor uncharted points & edges.
The Feedback heuristic add/remove score value to points & edges that are “in use” by other previously computed paths.
🝰 Inertia
Favor active direction preservation.
The Inertia heuristic uses the ongoing traversal data to try and maintain a consistent direction, as if the algorithm had “inertia”.
🝰 Steepness
Favor flat trajectories.
The Steepness heuristic uses the edge angle against an up vector to compute a dot product that is used to determine whether the edge should be considered flat or not.
🝰 Azimuth
Favor edges directed toward the goal.
The Azimuth heuristic attempt to force the path to always aim toward the goal.
🝰 Least Nodes
Favor traversing the least amount of nodes.
The Least Nodes heuristic favor node count traversal over anything else.