Digital twins in AI-controlled navigation tasks for autonomous UAV swarm

The article presents the concept and architecture of digital twins (DT) in the tasks of autonomous swarm navigation for unmanned aerial vehicles (UAVs) controlled by artificial intelligence. Study demonstrated that the effective operation of a drone swarm under conditions of disrupted or absent communication with the ground center is enabled by the functional distribution of DT components between the ground center and onboard levels of AI agents. Mathematical models of ground center’s DT provide strategic modeling, training, mission simulation, and post-mission analysis, while onboard AI agents focus on local adaptation, diagnostics, environmental reconstruction, and cognitive behavior control. Special attention is paid to the interface module of the DT, which provides asynchronous interaction with the ground infrastructure. A functional division on the swarm-level, environment, mission, telemetry, and agent-level DTs is proposed. The effectiveness of the “Learn–Simulate–Deploy–Adapt” cycle for continuous improvement of swarm systems in the context of electronic warfare (EW) and dynamic operational environments was justified. The results were partially supported by the National Research Foundation of Ukraine, grant No. 2025.06/0022 “AI platform with cognitive services for coordinated autonomous navigation of distributed systems consisting of a large number of objects”.