地图不确定性下的信念感知多智能体路径规划
Belief-Aware Multi-Agent Path Finding under Map Uncertainty
- Symbotic Inc.（Symbotic公司）
- Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对地图不确定性下的多智能体路径规划问题，提出MAGIC框架，利用高斯马尔可夫随机场和高斯信念传播在线更新可通行性信念，构建绕行感知代价，在96.3%的实例上降低执行总代价，适用于大规模场景。
AI中文摘要：
多智能体路径规划（MAPF）旨在共享环境中为多个智能体寻找无碰撞路径。经典MAPF假设所有静态障碍物事先已知，但真实环境可能因物体掉落、液体溢出或其他局部扰动而意外变化。当此类变化在空间上相关时，一次观测可以提供超出观测位置的可通行性估计信息。先前的方法通过应急计划或基于直接观测的重新规划来处理可通行性不确定性，但未利用这种空间依赖性来推断附近未观测位置的可通行性。因此，它们无法利用一次观测来预判附近可能引发后续代价高昂的重新规划的未观测障碍物。我们关注信念感知MAPF，其中地图差异在执行期间固定但初始未知，且观测信息可能超出观测位置。我们提出多智能体高斯信念推理协调（MAGIC）框架，该框架基于智能体的观测在线更新关于可通行性的共享信念。MAGIC使用高斯马尔可夫随机场和高斯信念传播来近似推断可通行性，并为标准MAPF规划器构建绕行感知代价。我们在MAPF基准上的实验表明，与现有方法相比，MAGIC在96.3%的实例上降低了执行总代价，涵盖多个规划器系列和最多800个智能体的团队，展示了其在大规模MAPF问题上的适用性。
英文摘要：
Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedly due to fallen objects, spills, or other local disturbances. When such changes are spatially correlated, an observation can inform traversability estimates beyond the observed location. Prior approaches address uncertainty in traversability through contingent plans or replanning based on direct observations, but do not leverage this spatial dependence to infer the traversability of nearby unobserved locations. As a result, they cannot use one observation to anticipate nearby unobserved obstacles that may cause costly rerouting later. We focus on Belief-Aware MAPF, where map discrepancies are fixed during execution but initially unknown, and observations can be informative beyond the observed location. We propose Multi-Agent Gaussian belief Inference for Coordination (MAGIC), a framework that updates a shared belief about traversability online based on agents' observations. MAGIC uses a Gaussian Markov Random Field and Gaussian Belief Propagation to approximately infer traversability and construct detour-aware costs for standard MAPF planners. Our experiments on MAPF benchmarks show that MAGIC reduces the executed sum of costs compared to existing approaches on 96.3% of instances, across several planner families and teams of up to 800 agents, demonstrating its applicability to large-scale MAPF problems.
