Communication-Aware and Safety-Aware UAV Control via Predictive Latent Models

Abstract

This article presents a communication-aware and risk-aware predictive latent control (CRPL) framework for unmanned aerial vehicle (UAV) systems operating under partial observability and uncertain environment dynamics. CRPL integrates a joint-embedding predictive architecture (JEPA) with probabilistic communication and safety constraints to jointly optimize UAV motion and transmission power. The learned latent model generates recursive multi-step rollouts, enabling the controller to anticipate future motion, channel degradation, and collision risk. These predictions are incorporated into a unified safety-aware optimization framework for proactive, energy-aware trajectory and communication adaptation. Simulation results show that CRPL closely approaches the performance of an oracle analytical predictive controller and outperforms reactive constrained and unconstrained baselines under limited bandwidth and dynamic uncertainty. In the bandwidth-limited regime, CRPL reduces terminal error, i.e., the final UAV-to-goal distance, by up to a factor of approximately $3$ and outage duration by up to approximately $18$, while also lowering communication energy and collision risk. These improvements are achieved with only a moderate motion-energy overhead, demonstrating a favorable trade-off among mobility effort, communication reliability, and operational safety.