Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Path Planning
Roadmap Generation
Continuous Environments
Graph Density
Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network
🤖 AI Summary
This study addresses the challenge of balancing roadmap density and solution quality in multi-agent path planning by proposing an automated shared roadmap generation framework based on heterogeneous graph neural networks. The method exploits task permutation invariance to enable roadmap reuse and is trained under supervision from occupancy density maps aggregated over expert trajectories, yielding compact yet coordination-aware roadmaps. Experimental results demonstrate that the proposed framework reduces both runtime and graph size by at least 40%, while significantly enhancing global connectivity and interaction reasoning capabilities. Consequently, it effectively improves planning efficiency and solution quality in densely populated scenarios.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Heterogeneous Graph Neural Network
Multi-Agent Path Planning
Shared Roadmap Generation
Occupation Density Maps
Graph Pruning
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