Mid-air refuelling stands as one of the most demanding operational procedures in modern military aviation. To improve safety and training outcomes, the US Air Force is exploring artificial intelligence to automatically identify mistakes and performance errors during fuel transfer missions. The proposed technology will examine video feeds captured directly by tanker aircraft cameras to spot operational errors that conventional human assessments might easily overlook.
Current performance evaluations rely heavily on manual video reviews, instructor observations, and post-flight crew debriefs. These traditional methods often lead to inconsistent judgments or missed details during high-stakes maneuvers. By leveraging computer vision and machine learning, the new software will monitor critical flight parameters including relative positioning, approach velocity, aircraft speed, and alignment between the tanker and receiving aircraft. The system will also track the refuelling boom against aircraft trajectories to flag dangerous deviations in real time.
Initial development efforts focus primarily on the KC-46 Pegasus tanker, which already utilizes advanced camera systems for remote fuel transfer operations. Rather than replacing human instructors entirely, the automated platform will generate precise performance analytics featuring graphical overlays, chronological displays, and color-coded performance maps. Military planners eventually intend to integrate these AI evaluation tools with existing flight simulators and virtual reality training environments, creating a standardized framework for aviation instruction across the Department of Defense.
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