Delay-Aware UAV Swarm Formation Control via Imitation Learning from ARD-PF Expert Policies

作者：Rodolfo Vera-Amaro, Alberto Luviano‐Juárez, Mario E. Rivero-Ángeles · 发表于：Drones · 年份：2026 · DOI：10.3390/drones10010034 · 被引用次数：3 · 研究领域：Distributed Control Multi-Agent Systems、UAV Applications and Optimization、Reinforcement Learning in Robotics

This paper studies delay-aware formation control for (unmanned aerial vehicle) UAV swarms operating under realistic air-to-air communication latency. An attractive–repulsive distance-based potential-field (ARD-PF) controller is used as an expert to generate demonstrations for imitation learning in multi-UAV cooperative systems. By augmenting the training data with communication delay, the learned policy implicitly compensates for outdated neighbor information and improves swarm coordination during autonomous flight. Extensive simulations across different swarm sizes, formation spacings, and delay levels show that delay-robust imitation learning significantly enlarges the probabilistic stability region compared with classical ARD-PF control and non-robust learning baselines. Formation control performance is evaluated using internal geometric error, global offset, and multi-run stability metrics. In addition, a predictive delay–stability model is introduced, linking the maximum admissible communication delay to swarm size and inter-agent spacing, with low fitting error against simulated stability boundaries. The results provide quantitative insights for designing communication-aware UAV swarm systems under latency constraints.