Integrated Adaptive Aiding System for UAV Control and Related Applications

The military use of Uninhabited Aerial Vehicles (UAVs) has been increasing dramatically. The subsequent increase in demands on UAV operators leads to stress and fatigue, and consequently results in operator error, mission performance decrement, and even loss of vehicles or lives. The U.S. military reports mishap rates for UAVs up to 30 times higher than for manned aircraft.      The main goal of this Phase II program is to develop a Closed-Loop Adaptive Aiding System (CLAAS) that improves UAV mission performance by adaptively reducing operator cognitive workload. Using technology developed in part for the USAF under a research program for enhanced C2ISR operator performance, we utilize QUASARs breakthrough wearable dry-electrode based physiological monitoring system to measure physiological parameters to classify operator cognitive states (engagement, workload, fatigue, and stress) during a multi-UAV simulation task. The cognitive states are combined with present and future mission context to predict the probability of imminent operator error. To close the loop, this predicted performance will feed back to proactively mitigate UAV task demands in order to reduce operator cognitive workload and hence enhance mission performance.       Such a system designed to adaptively mitigate operator-error would be of considerable interest to the DoD or air traffic control. BENEFIT: The benefits of this system are increased efficiency and safety of UAV control by human operators. Operators will be able to control more UAVs at one time at higher operational effectiveness. There is a large potential market in air traffic control. Commercial UAV applications (such as border monitoring) will also benefit, and there are also potential applications in industries such as oil exploration, in which operators control underwater unmanned vehicles.