The 2014 Langley Aeronautics Academy, which consisted of a multi-disciplinary team of twelve student researchers, was challenged to design, build, and test fly a cost effective, fully autonomous, dual-use, small Unmanned Aerial System (sUAS) in a ten-week time frame. This airborne science platform was designed for applications in Precision Agriculture (PA) and Search and Rescue (SAR) missions, while legally operating within the National Airspace System (NAS). Two tiers of success were defined for each application, the threshold and the objective. The threshold represented the adequate level of performance, while the objective represented the ideal operating capability of the respective application. The threshold level of success for the SAR mission was to demonstrate the flight endurance to transit two kilometers to a search grid, image a four square kilometer area while transmitting a real-time video downlink of the search area to a ground station, and then transit another two kilometers to return to the point of launch. Additionally, the SAR objective required the sUAS to autonomously locate people in the search grid. The threshold level of success for the PA application was to demonstrate the flight endurance to image a one thousand acre farm in a single flight and to discern stressed plants to a resolution of one foot, through use of the Normalized Difference Vegetation Index, while flying at altitudes up to 400 feet. Additionally, the objective was to autonomously provide an orthorectified map indicating the location of the stressed plants. Self-set goals were then established as PA and SAR applications were researched and professionals in the respective fields were interviewed to determine opportunities for improvement. Human factors engineering was a main consideration throughout the project lifecycle, from preliminary design through test flight, as this system was developed for use by individuals without technical experience. Upon investigation, it was determined that most sUAS are highly specialized in operation, and those which possess multi-purpose capabilities are expensive. This paper outlines the development, systems integration, and testing of a highly adaptive, economically viable, autonomous sUAS with the goal of breaking the barrier between performance and expense.
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