I've used it in the past and I actually got it running pretty fast, even though it's written in python. So I was optimistic about replacing my less than stellar dumb path-finding with A*.
However I ran in to some problems.
First is the fact that I'm working with quite a fine graph, each infantry man takes up one tile, and tanks and the like take up several (a big tank takes up something like a 12x12 chunk of tiles. When you're working with a graph which might be several hundred tiles square, you get slow performance.
When I've used it before I worked with either a pretty course grid, or with navigation nodes.
|[a* with grid]|
|[a* with nodes]|
Working outdoors with such a fine and almost unlimited grid makes the algorithm work too hard, especially when you have a lot of agents.
Looks like I'll have to think more about this. One option would be to use a course grid or node array alongside my fine grid path-finding. That way the agents are just finding their way from one node to another. The curse grid could take in to account such things as rivers and buildings while ignoring vehicles and infantry. If the way to the next node is blocked the agent would just wait for the blockage to clear using the right of way system I mentioned last time.
In fact this is something I prototyped around 2 years ago, using node based a_star path-finding and a path smoothing algorithm.
|[node path and smoothing]|
I think I'm going to leave this for now though, as this doesn't form a part of the basic agent movement actions, rather a part of agent behavior, which will be controlled by the agents finite state machine. The agent can continue to use the dumb pathfinding, and a* will be used to supply some intermediate waypoints when long distance movement is required (by breaking down the agent destination list in to reachable steps).