# 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

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2610-22085
> 频道：papers | 更新日期：2026-10-15 | 标签：arxiv, cs.RO
> 核心摘要：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。

# 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

::: v-pre
> 原文链接：[Coverage-Preserving Self-Healing in Partially Observable 3D UAV Swarm Networks via an Advection-Diffusion Graph Transformer](https://arxiv.org/abs/2610.22085)（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
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## 原始参考来源 (Sources)

- [arXiv 2610.22085：Coverage-Preserving Self-Healing in Partially Observable 3D UAV Swarm Networks via an Advection-Diffusion Graph Transformer](https://arxiv.org/abs/2610.22085)