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.

提出了基于 MA-RRT * 算法的多智能体路径规划问题解决方案，该算法在解决真实世界中大型但稀疏环境的多飞机防撞等任务时性能很好，能够有效利用采样机制，比以往的正向搜索方法更具可扩展性。