I posted this question on stack overflow first, but I guess no one is very interested in video games there...

What are some path finding algorithms used in games of all types? (Of all types where characters move, anyway) Is Dijkstra's used a whole lot? I would think not, as it doesn't actually trace out the steps to take to get somewhere, right? If I'm understanding it right, it only determines which object is the closest. I'm not really looking to code anything; just doing some research, though if you paste pseudocode or something, that would be fine (I can understand Java and C++). I'm basically looking for a quick overview of path finding in general.

I know A* is like THE algorithm to use in 2D games. That's great and all, but what about 2D games that are not grid-based? Things like Age of Empires, or Link's Awakening. There aren't distinct square spaces to navigate to, so what do they do?

What do 3D games do? I've read this thingy http://www.ai-blog.net/archives/000152.html, which I hear is a great authority on the subject, but it doesn't really explain HOW, once the meshes are set, the path finding is done. IF A* is what they use, then how is something like that done in a 3D environment? And how exactly do the splines work for rounding corners?

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    \$\begingroup\$ I think this question is too open ended for SE's Q&A format. FAQ \$\endgroup\$ Apr 25, 2012 at 21:29
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    \$\begingroup\$ The games you mentioned must break down the map into nodes for A* in one way or another. That break down process don't have to involve squares grids and there are many ways to do it.Check this vid youtube.com/watch?v=nGC_kBCoHYc, a good game makes it so that players can't tell what they're actually doing behind the scenes. \$\endgroup\$ Apr 25, 2012 at 21:31
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    \$\begingroup\$ There are a lot of questions here, so I can't really write an answer, but I'll note that Dijkstra's does return a path, and most pathfinding algorithms are multipurpose. You convert your world, 2D or 3D, into a connected graph, and run a pathfinding algorithm on it. \$\endgroup\$ Apr 25, 2012 at 21:58
  • \$\begingroup\$ Just for reference: I answered the question at Stack Overflow. \$\endgroup\$
    – Julian
    Apr 25, 2012 at 23:56
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    \$\begingroup\$ Allow me to rant. This question got 4 upvotes on SO, compared to 4 close votes here on GDSE. I can't help but feel that moderators on this site are being overly aggressive. Sure, I can see how the question goes against the guidelines specified in the FAQ, but quoting, those guidelines are in place in order to prevent from diminishing the usefulness of our site. This question has been favorited 3 times already which is proof that it has been useful to some users. So I can't help but feel that voting to close it and risking an eventual removal, is a lot more counterproductive. \$\endgroup\$ Apr 27, 2012 at 19:39

1 Answer 1


Too many questions at once, so it's hard to give a concrete answer but to discuss a few of these topics. I'll divide the answer in two and try to address it as best as I can. I don't claim any of these lists to be complete, but they're some of the different methods I could remember.

Part 1 - Pathfinding Algorithms

For starters, there are many ways to implement pathfinding, but not all of them return the shortest path, or are efficient or even reliable. For instance:

  • Primitive methods that don't "look ahead" and take one step at a time:

    • Random backstepping - Take one step at a time in the direction of the goal. If an obstacle is encountered try to work around it by backstepping a bit in a random direction and then trying again. Not reliable at all and will get stuck in a multitude of situations.

    • Obstacle tracing - Other approach, similar to random backstepping but instead of moving back randomly, start tracing around the object once a collision is found, as if you had the right hand stuck to the wall and had to move touching it. Once there's no collision continue moving in the goal's direction. Once again can get stuck in many situations.

  • Methods that look ahead to find the entire path at once:

    • Breadth First Search - Simple graph traversal by visiting each layer of children at a time, stop when path is found. If the graph is unweighted (i.e. the distance between each adjacent node is always the same) it finds the shortest path although not too efficiently. For weighted graphs it might not return the shortest path, but will always find one if it exists.

    • Depth First Search - Another way to traverse a graph, but instead of taking it layer by layer, the algorithm tries to search deep into the graph first. This method can have problems if the depth of the search is not limited, especially when using a recursive implementation, which may lead to a stack overflow, so it is usually safer to implement it iteratively using a stack.

    • Best First Search - Similar to Breadth First Search but uses an heuristic that chooses the most promising neighbor first. The path returned may not be the shortest, but it's faster to run than breadth first search. A* is a type of Best First Search.

    • Dijkstra's Method - Keeps track of the total cost from the start to every node that is visited, and uses it to determine the best order to traverse the graph. Works with weighted graphs and returns the shortest path, but might involve a lot of searching.

    • A* - Similar to Dijkstra but also uses an heuristic to estimate how likely each node is close to the goal, in order to make the best decision. Because of this heuristic, A* finds the shortest path in a weighted graph in a much more timely manner.

  • Then there are variations of A* (or pathfinding optimizations in general) that make it faster or more adapted to certain circunstances, such as (see related answer and a comprehensive list on cstheory.SE):

    • LPA* - Similar to A* but can more quickly recalculate the best path when a small change to the graph is made
    • D* Lite - Based on LPA*, it does the same thing, but assumes the "start point" is a unit moving towards the finish while graph changes are being made
    • HPA* (Hierarchical) - Uses several layers at different abstraction levels to speed up the search. For instance, an higher level layer may simply connect rooms, while a lower level layer takes care of avoiding obstacles.
    • IDA* (Iterative Deepening) - Reduces memory usage in comparison with regular A* by using iterative deepening.
    • SMA* (Simplified Memory-Bounded) - Only makes use of available memory to carry out the search.
    • Jump Point Search - Credits to Eric in the comments for mentioning it! Speeds up pathfinding on uniform-cost grid maps (link).

Part 2 - Search Space Representation

And finally to address this question:

I know A* is like THE algorithm to use in 2D games. That's great and all, but what about 2D games that are not grid-based?

Two big misconceptions here! In fact:

  1. A* doesn't care if the game is 2D or 3D, and is equally appropriate for both cases.
  2. A* works under any graph representation, so it doesn't care whether the world is a grid or not.

So if the world doesn't need to be a grid, in what other ways can you represent it? Here's a brief overview of ways to partition the world space for pathfinding, and most of these work both for 2D and 3D alike:

  • Rectangular grid - Partition world into regular grid of squares with each cell in the grid being one node in the graph, and the connection between two unobstructed nodes is an edge.

    enter image description here

  • Quadtree - Another way to partition the space, but instead of partitioning into a grid of regular sized cells, partition in four, then recursively divide each of these in four again. Adding a third dimension makes it an octree.

    enter image description here

  • Convex polygons - Partitioning the walkable area into a mesh of interconnected convex polygons. Each polygon becomes a node, and shared edges are the edges of the graph. These can be triangles for instance, and sometimes even be a mesh created by an artist when creating the level assets. Often referred to as a navigation mesh. See this link. Here's a very popular navigation mesh construction toolset: Recast.

    enter image description here

  • Points of visibility - The most common way is to place a node just outside each of the obstacle's convex vertices, and then connect each pair of nodes that can see each other. Check this link. The nodes don't have to be the vertices though, and can be placed manually by the designer in the map. In that case, the system is frequently referred to as a waypoint graph.

    enter image description here

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    \$\begingroup\$ Two links: 1) Mikko Mononen has done path finding work Killzone 3, and he has a very nice blog where he documents the development process of Recast (navmesh generator) and Detour (path finding toolkit), both under MIT license and used for example in Kingdoms of Amalur: Reckoning. 2) Jump point search is, I think, one of the biggest recent developments in grid-based path-finding. \$\endgroup\$
    – Eric
    Apr 26, 2012 at 10:15

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