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论文arXiv 2610.2208513 天前

通过 Advection-Diffusion Graph Transformer 实现部分可观测 3D UAV 集群网络的覆盖保持型自愈 ​

面向受损后全局可观测性受限的UAV集群通信网络，提出AdDiff-GT并在分区仿真场景验证拓扑恢复与空间保持。

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原文链接：Coverage-Preserving Self-Healing in Partially Observable 3D UAV Swarm Networks via an Advection-Diffusion Graph Transformer（arXiv 2610.22085 · 发布日期：2026-10-15）

Coverage-Preserving Self-Healing in Partially Observable 3D UAV Swarm Networks via an Advection-Diffusion Graph Transformer

面向受损后全局可观测性受限的UAV集群通信网络，提出AdDiff-GT并在分区仿真场景验证拓扑恢复与空间保持。

核心要点 ​

  • 针对复杂对抗环境下严重网络分区，关注UAV集群通信网络韧性与自愈。
  • GCS可由稀疏本地报告推断全局故障，且无需传输位置敏感坐标。
  • AdDiff-GT在节点-超节点混合图中融入平流-扩散动力学，结合局部传播与全局注意力。
  • 仿真显示其较六个代表性基线实现有效拓扑恢复和空间保持；元学习降低在线规划开销。

验证范围与局限：仅在8000个分区仿真场景（含5000个大规模案例）中验证。

英文摘要 (Abstract) ​

This paper investigates the resilience and self-healing of unmanned aerial vehicle swarm communication networks under severe network partitioning in complex and adversarial environments. To address limited global observability after damage, we develop a damage-state inference mechanism that enables the ground control station (GCS) to infer global failures from sparse local reports without transmitting location-sensitive coordinates. To recover multi-cluster partitioned topologies, we then propose AdDiff-GT, a physics-inspired graph Transformer that incorporates advection-diffusion dynamics into a node-supernode hybrid graph. Local message propagation captures intra-cluster structural relationships, while global attention models inter-cluster interactions. A weighted composite loss jointly considers connectivity restoration, maneuvering cost, motion consistency, inter-cluster coordination, and spatial preservation during model training. We further develop a meta-learning strategy for transferable initialization across damage patterns and swarm scales, enabling efficient online GCS adaptation and distributed airborne inference. Simulations on 8,000 partitioned scenarios, including 5,000 large-scale cases, show that AdDiff-GT achieves effective topology recovery and spatial preservation over six representative baselines. Meta-learning reduces online planning overhead with a minor increase in recovery steps, balancing prior knowledge transfer and online adaptation.

作者：Dongbin Jiao, Zhihao Li, Zongsheng Geng, Dongdong Zhao, Shengcai Liu, Shi Yan

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2610.22085：Coverage-Preserving Self-Healing in Partially Observable 3D UAV Swarm Networks via an Advection-Diffusion Graph Transformer访问 2026-10-21