Pathfinding

Modules & Documentation

This section is dedicated to broader pathfinding topics & modules. Node specifics can be found on their dedicated node page.

Pathfinding modules for Pathfinding nodes. Key steps include heuristic computation, goal picking, and search algorithms finding the best path based on weighted connections. 🝰 Heuristics and their weights are key to the operation. Note: Plot nodes handle point datasets differently, finding a path through each point in order.

Plot Edges Pathfinding

Find a path that goes through multiple chained points.

How pathfinding works

Although details vary a bit depending on the selected ⊚ Search algorithm, the basic gist is, for each path & cluster:

🝓 Goal Pickers will find a suitable Seed and Goal point within the cluster.
The Search Algorithm will then find the best path that goes from Seed to Goal, accounting for its internal maths, and using 🝰 Heuristics as to determine whether one connection is better than another.

Note: The Seed and Goal node are picked based on closest distance to input positions.

Starting from the seed, each “next step” is weighted according to the V Vertex weight and the E Edge weight that connects to it.
The search returns the path found with the lowest possible weight, or score.

While the selected search algorithm is important, 🝰 Heuristics are more critical to the operation, as user-defined weights can drastically change and shape the path deemed best by the search.

Note: The Plot nodes variations don’t have a goal picker and instead process each point Dataset as a list of points to go through from start to finish. The first point is the initial seed, the last point is the final goal, and then a path is found that goes through each point in-between, in order.