Why you need to learn the Flow Field algorithm

As said in the last post, I've read a lot about pathfinding. One of the pathfinding algorithms I found was the Flow Field. A Flow Field covers the entire grid (if a cell is not an obstacle and if a cell can be found from the start position), so while A* is finding the shortest path from the start position to a goal position, a Flow Field is inspired by water and is finding a path from each cell to the start position. It looks like this:

In the image above, the darker a cell is, the further away it is from the start position, which is the red sphere. The blue lines is pointing in the direction of the shortest path. But you can also add more start positions, which is actually speeding up the algorithm. So if you want to find the shortest path to the nearest door, then you can use the Flow Field.

A Flow Field is useful if you have a lot of units trying to find their way through your map, and was used in the game Planetary Annihilation. What they were doing was to divide the area into sectors, then use A* to find the shortest path between the sectors, and then they used a Flow Field to find the shortest paths through a sector. It looks like this:

But that's not it. A few years ago I made a self-driving car in Unity after reading a report on the Hybrid A* algorithm. One of the heuristics used by the Hybrid A* algorithm was something they called Dynamic Programming. I realized that Dynamic Programming is actually a Flow Field, so I updated my self-driving car with that algorithm. The 160x160 Flow Field looks like this and is generated in around 0.05 seconds:

You can also use the Flow Field algorithm to help you with obstacle detection. In the Hybrid A* algorithm, it's obviously important to quickly find the distance to the closest obstacle, and you can generate these distances fast once in the beginning. That Flow Field looks like this: