多智能体多目标路径规划首次实现多项式时间求解，突破传统算法瓶颈。
Solving Multi-Agent Multi-Goal Path Finding Problems in Polynomial Time
- 自动生成任务分配，动态优化路径避免冲突
- 离散场景下可在多项式时间内完成最优路径规划
- 适合大规模智能体协同任务，如无人机编队、仓储调度
本文研究在无向图（如网格）中为一组智能体规划多目标任务。与传统多智能体路径规划不同，本方法自主完成目标到智能体的分配。对于欧氏平面中的点状智能体连续运动情况，可逼近最优解；在存在节点和边冲突的离散情形下，证明问题可在多项式时间内求解，令人意外，因一般车辆路径问题为NP难。我们实现了相应的规划器，生成无冲突且优化的路径。全局分配策略显著减少冲突，剩余冲突通过改进‘蚂蚁上棍’概念、解决局部分配问题、交错路径以及将已到达智能体移出目标位置等方法处理。
原文摘要 · Abstract (English)
In this paper, we plan missions for a fleet of agents in undirected graphs, such as grids, with multiple goals. In contrast to regular multi-agent path-finding, the solver finds and updates the assignment of goals to the agents on its own. In the continuous case for a point agent with motions in the Euclidean plane, the problem can be solved arbitrarily close to optimal. For discrete variants that incur node and edge conflicts, we show that it can be solved in polynomial time, which is unexpected, since traditional vehicle routing on general graphs is NP-hard. We implement a corresponding planner that finds conflict-free optimized routes for the agents. Global assignment strategies greatly reduce the number of conflicts, with the remaining ones resolved by elaborating on the concept of ants-on-the-stick, by solving local assignment problems, by interleaving agent paths, and by kicking agents that have already arrived out of their destinations
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