Drones get smarter about what to sense and when, cutting operational costs
Researchers developed a new framework that lets drones automatically decide what to observe and communicate in real-time, reducing both control and sensing expenses. The approach could reshape how autonomous systems manage limited battery and bandwidth — critical economics for commercial drone operators and 6G infrastructure planners.
Originaltitel: Active Inference Framework for Closed-Loop Sensing, Communication, and Control in UAV Systems
Integrated sensing and communication is a core technology for 6G, and its application to closed-loop sensing, communication, and control (SCC) enables various services. Existing SCC solutions often treat sensing and control separately, leading to suboptimal performance and resource usage. In this work, we introduce the active inference framework into SCC-enabled uncrewed aerial vehicle systems for joint state estimation, control, and sensing resource allocation. By formulating a unified generative model, the problem reduces to minimizing variational free energy for inference and expected free energy for action planning. Simulation results show that both control cost and sensing cost are reduced relative to baselines.