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Hi Pablo,

We've definitely thought about this to some extent, but if want you really want to do is 2D navigation sampling-based planners are probably not the best tool. Sampling based planners are great for getting feasible paths in high dimensional spaces, but the path that gets returned can be of arbitrarily bad quality as judged by metrics like path distance. This means you may end up driving across the room and back again to go to a point that's very close to you, which is generally not what you want from your planner. You can deal with this to some extent by using path smoothing, but you still may not get a path anywhere near the optimal one. The search-based planners like A*, on the other hand, will produce guaranteed optimal paths or bounded optimal paths, where you know that you are within a factor of the optimal path. If you are getting optimal paths in very short times in 2dnav then there's not really a lot of reason to move to a sampling-based planner.

In my opinion a general rule of thumb is that if you can run an optimal search-based planner in the amount of time you can allocate for planning then there's no reason to consider another planning strategy. If your search spaces are difficult enough in terms of dimensionality, your time available to plan limited enough, or you don't care about path quality then ompl may start to make more sense.

There's no ompl adaptor for the global_planner interface that I know about - if you are really interested I can make a few suggestions for how to go about writing such a things.