Bridging the virtual and physical: A digital twin framework for reliable decision-making in UAV swarms

Unmanned aerial vehicle (UAV) swarms operate as collaborative, networked systems with collective intelligence, finding applications in agriculture, transportation, and military surveillance. Traditional human-in-the-loop control of UAV swarms suffers from high operator cognitive load and slow decision cycles. We proposes a digital twin-driven framework that enables intelligent and reliable decision-making for UAV swarms. By establishing a real-time, interactive, and co-evolving virtual environment, the framework transitions swarm supervision from “human-in-the-loop” to “human-on-the-loop” mode. Experimental validation on a multiple-UAV testbed demonstrates that the proposed framework maintains centimeter-level trajectory accuracy, reduces threat response time by 37.3%, and achieves near-zero manual intervention during nominal missions. The framework thus enables faster, more reliable, and scalable autonomous swarm operations by shifting operators from direct control to high-level supervision.