AI 中文总结
针对间歇空-空通信下无人机集群协同侦察的问题，提出带记忆增强的潜在语义状态估计框架，采用集中式训练与分布式执行，仿真显示其性能接近全连通集群且鲁棒性良好。
AI 中文摘要
多无人机协同侦察常受间歇空-空通信阻碍，链路中断会导致探索不协调与建图冗余。现有方法依赖显式交换高维空间数据或原始观测，开销巨大，且中断时往往退化为反应式个体探索。本文提出一种带记忆增强的框架，每架无人机维护分解为地图、任务与记忆分量的结构化潜在状态；链路中断时，基于记忆状态的生成预测器在潜在空间推断替代的同伴信息，使估计任务更易处理且与协同目标直接对齐。该框架采用集中式训练与分布式执行范式端到端训练，仿真结果表明，所提框架性能接近全连通集群，且在各类链路故障条件下均保持鲁棒性。
英文摘要
Cooperative multi-unmanned aerial vehicle (UAV) reconnaissance is often hindered by intermittent air-to-air communications where link dropouts lead to uncoordinated exploration and redundant mapping. Existing approaches rely on explicit exchange of high-dimensional spatial data or raw observations, incurring significant overhead, and often revert to reactive individual exploration during outages. This paper proposes a memory-augmented framework in which each UAV maintains a structured latent state decomposed into map, task, and memory components. During dropout, a generative predictor conditioned on the memory state infers substitute peer messages in the latent space, making the estimation task more tractable and directly aligned with the cooperative objective. The framework is trained end-to-end under the centralized training with decentralized execution paradigm. Simulation results demonstrate that the proposed framework closely matches the performance of a fully connected swarm, while remaining robust across a wide range of link failure conditions.
Comments6 pages, 1 table. Accepted for poster presentation at the IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2026
