Multi-agent RRT*: Sampling-based Cooperative Pathfinding (Extended Abstract)
This addresses path planning for teams of mobile robots, but it is incremental as it builds upon an existing single-agent algorithm.
The paper tackles cooperative pathfinding for mobile agents like robots by proposing MA-RRT*, a novel algorithm based on RRT*, and shows it offers better scalability than classical forward-search methods in large, sparse environments typical of real-world applications such as multi-aircraft collision avoidance.
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.