Latent Semantic State Estimation for Reliable Swarming of UAVs under Intermittent Connectivity

Abstract

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.