Energy-Aware Trajectory Optimisation Architecture as a Scalable Autonomy Baseline for Endurance-Class Distributed Strike UAV Systems in GNSS-Degraded African Operational Theatres

Long-endurance loitering munition Unmanned Aerial Vehicles (UAVs) have emerged as decisive force multipliers within distributed reconnaissance–strike convergence architectures by enabling persistent surveillance, adaptive engagement timing and stand-off precision strike capability across contested electromagnetic environments. However, propulsion-energy constraints remain the dominant limitation governing mission persistence and terminal engagement readiness, particularly under degraded Global Navigation Satellite System (GNSS) conditions. This paper presents an integrated energy-aware trajectory optimisation architecture combining glide-segment scheduling, adaptive loiter-geometry selection, wind-vector-aligned routing, multisensor navigation fusion, and receding-horizon Model Predictive Control (MPC) guidance to maximise time-on-station while preserving strike feasibility. Closed-loop simulation results demonstrate persistence improvements of up to 46% relative to conventional circular-orbit loiter strategies. The proposed framework is positioned as a scalable autonomy baseline for endurance-class distributed strike UAV systems operating in GNSS-degraded African operational theatres characterised by sparse navigation infrastructure, extended mission radii, and contested electromagnetic environments.