INTELLIGENT MID-AIR WIRELESS CHARGING SYSTEM FOR UNMANNED AERIAL VEHICLES USING HYBRID POWER TRANSFER AND AI-BASED PREDICTIVE ALIGNMENT

This paper has presented a comprehensive intelligent mid-air wireless charging system for Unmanned Aerial Vehicles, addressing the fundamental limitation of restricted battery capacity that constrains operational endurance and mission effectiveness. The proposed architecture integrates three key innovations: a hybrid wireless power transfer mechanism combining inductive coupling and microwave beam forming to achieve adaptive efficiency across variable separation distances; a multi-sensor fusion alignment system utilizing GPS, IMU, vision, and radar for precision positioning under diverse environmental conditions; and an AI-based predictive decision-making framework optimizing charging schedules and power allocation for scalable multi-drone operations. The mathematical models developed for power transfer efficiency, alignment error compensation, and battery state estimation provide rigorous design foundations and performance boundaries. Simulation results demonstrate that the hybrid system maintains end-to-end efficiency above 75% across the operational range of 0.1-3.0 m, with peak efficiency of 88% at short range. The intelligent alignment subsystem achieves sub-centimeter precision under clear conditions and maintains acceptable accuracy under turbulence and reduced visibility. Mission continuity simulations for an 8-hour surveillance scenario demonstrate 80% reduction in mission downtime compared to ground-based charging, with fleet availability maintained above 90% for configurations up to six child drones. The intelligent mid-air wireless charging system presented herein constitutes a significant step toward fully autonomous, self-sustaining UAV ecosystems capable of indefinite operational endurance in dynamic, unstructured environments.

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