发表机构
Institute of Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种可扩展的异构图神经网络框架，自动生成并评估共享多智能体路线图，通过剪除冗余节点和边，实现至少40%的运行时间和图大小缩减，同时可能找到更优解。
AI 中文摘要
连续环境中的多智能体路径规划（MAPP）通常依赖路线图来平衡安全性与搜索效率。然而，传统的路线图生成方法，如格子网格或标准采样方法，经常面临图密度与找到可行高质量解决方案可能性之间的权衡。本文提出了一种可扩展的异构图神经网络（GNN）框架，用于自动生成和评估共享的多智能体路线图。我们的模型将路径点、智能体位置和任务位置表示为异构图中的不同节点，从而能够推理全局连通性和智能体间的交互。通过训练于从专家求解器轨迹中聚合和收集的占用密度图，GNN学习识别关键兴趣点并剪除冗余节点和边。该过程生成一个紧凑、协调感知的路线图，该路线图对任务排列不变，并可重复用于多智能体取送货任务。实验结果表明，我们的框架可以减少规划工作量，并可能找到更好的解决方案，在密集路线图上实现至少40%的运行时间和图大小缩减。
英文摘要
Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation methods, such as lattice grids or standard sampling-based approaches, frequently face a trade-off between graph density and the likelihood of finding feasible, high-quality solutions. In this paper, we propose a scalable heterogeneous Graph Neural Network (GNN) framework for the automated generation and evaluation of shared multi-agent roadmaps. Our model covers the representation of waypoints, agent locations, and task locations as distinct nodes in a heterogeneous graph, allowing it to reason over global connectivity and inter-agent interactions. By training on occupation density maps aggregated and collected from expert solver trajectories, the GNN learns to identify critical points of interest and prune redundant nodes and edges. This process produces a compact, coordination-aware roadmap that is invariant to task permutations and is reusable for multi-agent pick and delivery tasks. Experimental results demonstrate that our framework can reduce planning effort and can potentially find better solutions, reaching at least 40% reduction in runtime and in graph size for dense roadmaps.
