Pathfinding algorithms

We like to think of AI as clever. In truth, she’s just following rules to move through space. Pathfinding is the rulebook. Without it, she’d bump into walls like a Roomba gone rogue.

Grids

Start with grids. We chop the world into squares, like graph paper. Each square is either walkable or blocked. Our AI checks neighbors, moves step by step, and keeps track of where she’s been. It’s simple, easy to debug, and fine for games with tiled maps. The downside is obvious—real life doesn’t line up neatly on graph paper.

Navigation meshes

So we draw shapes instead. Navigation meshes (“navmeshes”) cover open areas with polygons. She moves across them like stepping stones, finding routes that look more natural. A navmesh works better in 3D spaces, or anywhere maps don’t fit in boxes. It takes longer to set up, but our AI looks less like a board game piece.

Dijkstra’s algorithm

Now, how does she decide the best route? One answer is Dijkstra’s algorithm. It treats the map like a network of nodes, each with a cost to travel. She starts at the beginning, checks all paths, and records the cheapest so far. When she reaches the goal, the path is guaranteed to be the shortest. The tradeoff is speed—she checks a lot, even when the goal is close.

Decision-making

Pathfinding is really decision-making in disguise. Grids are easy, navmeshes are flexible, and Dijkstra’s is thorough. Each has limits. What matters is how fast she needs to move and how real we want it to feel.

A coder’s musing

We can admire her persistence, but we should also admit something. Most of the time, she’s not thinking at all. She’s just following the map we built. The magic is ours.