Multi-agent RRT: sampling-based cooperative pathfinding


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.

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@inproceedings{Cp2013MultiagentRS, title={Multi-agent RRT: sampling-based cooperative pathfinding}, author={Michal C{\'a}p and Peter Nov{\'a}k and Jir{\'i} Vokr{\'i}nek and Michal Pechoucek}, booktitle={AAMAS}, year={2013} }