AI 中文总结
本研究针对通用多智能体路径规划问题，提出AOC-CBS算法，该算法可 anytime-optimal 求解，实验表明其在接受有界最优性间隙时能将可扩展性从数十智能体扩展到数百个。
AI 中文摘要
许多研究领域存在共同结构：一组智能体各自追求自身目标，其行动必须协调以避免冲突。多智能体路径规划（MAPF）是该结构的具体实例，应用于仓库、道路交通和机场等场景。多数MAPF研究采用离散时间、圆形智能体共享单一空间图、每个智能体仅有一个目标且到达后必须停留的假设，这排除了异构智能体集群、非几何冲突、任务序列及完成任务后可继续移动的智能体。我们将MAPF公式推广以解除这些假设，并提出Anytime-Optimal Continuous-time Conflict-Based Search（AOC-CBS），这是一种针对通用MAPF的精确且解完备的求解器。AOC-CBS保证最终返回最优解，同时在运行期间报告带有已知最优性间隙上界的当前解；它可通过一系列修复函数进行配置，其中我们提出了Tier-Prioritized Safe Interval Path Planning（按层级优先的安全区间路径规划），且能利用多个处理器核心。我们在非凸智能体组成的混合集群上验证AOC-CBS，这些智能体沿平滑、符合动力学约束的轨迹移动。与精确求解器OC-CBS的初步实验在知名基准及我们从其采样的路线图上进行，结果显示，在接受有界最优性间隙的情况下，AOC-CBS在寻找最优解的性能相当，同时将可扩展性从数十个智能体扩展到了数百个。
英文摘要
Many research fields share a common structure: a set of agents, each pursuing its own goal, whose actions must be coordinated so that no two of them conflict. Multi-Agent Path Finding (MAPF) is a concrete instance of this structure, with applications from warehouses to road traffic and airports. Much of MAPF research assumes discrete time, circular agents sharing one spatial graph, a single goal per agent, and that an agent must remain at its goal once reached, precluding heterogeneous fleets, non-geometric conflicts, task sequences, and agents that move on after completing them. We generalise the MAPF formulation to lift these assumptions, and present Anytime-Optimal Continuous-time Conflict-Based Search (AOC-CBS), an exact and solution-complete solver for it. AOC-CBS guarantees the eventual return of an optimal solution, while reporting an incumbent with a known optimality gap upper bound throughout its runtime; it is configurable with a portfolio of repair functions, one of which we introduce (Tier-Prioritized Safe Interval Path Planning), and can exploit multiple processor cores. We demonstrate AOC-CBS on a mixed fleet of non-convex agents moving along smooth, kinodynamically feasible trajectories. Preliminary experiments against the exact solver OC-CBS, on well-known benchmarks and roadmaps we sample from them, show AOC-CBS is comparable at finding optimal solutions while extending scalability from the tens to the hundreds of agents when a bounded optimality gap is accepted.
Comments65 pages, 19 figures
