  Cooperative pathfinding is a problem of finding a set of non-conflicting
trajectories for a number of mobile agents. Its applications include planning
for teams of mobile robots, such as autonomous aircrafts, cars, or underwater
vehicles. The state-of-the-art algorithms for cooperative pathfinding typically
rely on some heuristic forward-search pathfinding technique, where A* is often
the algorithm of choice. Here, we propose MA-RRT*, a novel algorithm for
multi-agent path planning that builds upon a recently proposed
asymptotically-optimal sampling-based algorithm for finding single-agent
shortest path called RRT*. We experimentally evaluate the performance of the
algorithm and show that the sampling-based approach offers better scalability
than the classical forward-search approach in relatively large, but sparse
environments, which are typical in real-world applications such as
multi-aircraft collision avoidance.
