Multi-agent pathfinding (MAPF) is the problem of finding a set of
conflict-free paths for a set of agents. Typically, the agents' moves are
limited to a pre-defined graph of possible locations and allowed transitions
between them, e.g. a 4-neighborhood grid. We explore how to solve MAPF problems
when each agent can move between any pair of possible locations as long as
traversing the line segment connecting them does not lead to the collision with
the obstacles. This is known as any-angle pathfinding. We present the first
optimal any-angle multi-agent pathfinding algorithm. Our planner is based on
the Continuous Conflict-based Search (CCBS) algorithm and an optimal any-angle
variant of the Safe Interval Path Planning (TO-AA-SIPP). The straightforward
combination of those, however, scales poorly since any-angle path finding
induces search trees with a very large branching factor. To mitigate this, we
adapt two techniques from classical MAPF to the any-angle setting, namely
Disjoint Splitting and Multi-Constraints. Experimental results on different
combinations of these techniques show they enable solving over 30% more
problems than the vanilla combination of CCBS and TO-AA-SIPP. In addition, we
present a bounded-suboptimal variant of our algorithm, that enables trading
runtime for solution cost in a controlled manner.

多智能体路径规划中的任意角路径规划问题，利用连续冲突搜索算法和安全区间路径规划算法的最优算法及其可行解空间分割和多约束技术，能解决比传统组合更多的问题。