基于解耦和集成目标顶点排序的多目标多智能体路径搜索
Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering
摘要：
我们介绍了多目标多智能体路径寻找（MAPF $^{MG}$），它推广了标准的离散多智能体路径寻找（MAPF）问题。MAPF的任务是将无向图中的代理从其起始顶点导航到每个代理的一个单独的目标顶点，而MAPF $^{MG}$为每个代理分配多个目标顶点，并且任务是至少访问每个目标顶点一次。求解MAPF $^{MG}$不仅需要为各个代理找到无冲突路径，而且还需要确定访问代理的目标顶点的顺序，以便优化共同的目标，如成本之和。我们提出了两种新的算法，使用不同的范式来解决MAPF $^{MG}$：一个启发式搜索为基础的搜索算法称为汉密尔顿CBS（HCBS）和编译为基础的算法使用SMT范式，称为SMT汉密尔顿CBS（SMT-HCBS）。实验比较表明编译为基础的方法的局限性。
英文摘要：
We introduce multi-goal multi agent path finding (MAPF$^{MG}$) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MAPF$^{MG}$ assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MAPF$^{MG}$ not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized. We suggest two novel algorithms using different paradigms to address MAPF$^{MG}$: a heuristic search-based search algorithm called Hamiltonian-CBS (HCBS) and a compilation-based algorithm built using the SMT paradigm, called SMT-Hamiltonian-CBS (SMT-HCBS). Experimental comparison suggests limitations of compilation-based approach.
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