UAV

Opening — Why this matters now Drones have learned to fly cheaply, see broadly, and deploy everywhere. What they still struggle with is something far less glamorous: noticing small things that actually matter. In aerial imagery, most targets of interest—vehicles, pedestrians, infrastructure details—occupy only a handful of pixels. Worse, they arrive blurred, partially occluded, and embedded in visually noisy backgrounds. Traditional object detectors, even highly optimized YOLO variants, are structurally biased toward medium and large objects. Small objects are the first casualties of depth, pooling, and aggressive downsampling. ...

When language models take flight, consensus becomes not just possible, but programmable. Modern UAV swarms face the daunting task of coordinating across partial observability, adversarial threats, and shifting missions. Traditional Multi-Agent Reinforcement Learning (MARL) offers adaptability, but falters when role differentiation or semantic reasoning is required. Large Language Models (LLMs), meanwhile, understand tasks and intent—but lack grounded, online learning. RALLY (Role-Adaptive LLM-Driven Yoked Navigation) is the first framework to successfully integrate these two paradigms, enabling real-time, role-aware collaboration in UAV swarms. ...