经典多智能体路径规划的成本
The Cost of Classical Multi-Agent Path Finding
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中文总结 AI 辅助
本研究比较经典MAPF与连续时间MAPF_R，发现后者通过扩展移动动作在狭窄和开放地图上分别平均提升至少5%和17%的解质量，而提高分辨率仅恢复不到3%，揭示了经典表述的适用边界。
中文摘要 AI 辅助
多智能体路径规划（MAPF）是在共享空间中为多个智能体规划从各自起点到目标点的无冲突路径的问题。多年来，经典MAPF一直是主导性的问题表述，其离散时间和基于图的冲突假设可能简化了求解过程。这些假设限制了真正无碰撞解所适用的物理环境和智能体，同时也对解质量设置了上限，任何算法改进都无法突破这一上限。本研究探讨了经典MAPF表述在何种情境下会损失多少解质量。连续时间MAPF（MAPF$_R$）放宽了这些假设，使其成为在不同智能体数量和大小、图连通性、拓扑结构和分辨率下进行对比的自然对应表述。我们发现，连续时间和智能体形状考虑本身价值相对较小；它们的价值在于能够扩展移动动作的范围，在狭窄和受限地图上平均提高至少$5\%$的解质量，在具有开放空间的地图上平均提高$17\%$。在某些情况下，改进幅度超过$20\%$。将经典MAPF的地图分辨率加倍只能恢复不到$3\%$，这意味着损失的部分几乎无法通过更多计算来弥补。因此，这项工作为经典MAPF何时是合理的简化，以及何时MAPF$_R$能解锁显著更高质量的解决方案提供了见解。
英文摘要
Multi-Agent Path Finding (MAPF) is the problem of planning conflict-free paths for multiple agents in a shared space, each from its start to its goal. Classical MAPF has been the dominant formulation for many years, with its assumptions of discrete time and graph-based conflicts presumably easing the search for solutions. These assumptions limit the physical environments and agents for which a solution is truly collision-free, and also place an upper bound on solution quality that no algorithmic improvements can lift. This work investigates how much solution quality, and in what contexts, the classical MAPF formulation forfeits. Continuous-time MAPF (MAPF$_R$) relaxes these assumptions, making it a natural counter-formulation to compare against across various agent counts and sizes, and graph connectedness, topologies, and resolutions. We find that continuous time and agent shape consideration are worth relatively little on their own; their value comes from enabling an expanded range of move actions, on average improving solution quality by at least $5\%$ on narrow and constrained maps and $17\%$ on maps with open spaces. In some cases, the improvements exceed $20\%$. Doubling the map resolution with classical MAPF recovers less than $3\%$, meaning that little of what is forfeited can be bought back through more compute. This work therefore provides insight on when classical MAPF is a reasonable simplification, and when MAPF$_R$ unlocks significantly higher-quality solutions.
发表机构
- Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
