The proposed UAS is based on the quadplane architectural concept, combining multirotor and fixed-wing UAS to provide the best of both vehicles strengths. IT uses the multirotor vertical takeoff and…

The U.S. government has awarded a $73,786.24 grant to North Carolina Agricultural and Technical State University (NC A&T) for research on a hybrid quadplane unmanned aerial system (UAS) for search and rescue missions. The UAS combines multirotor and fixed-wing capabilities to provide vertical takeoff and landing with efficient cruise flight, aiming to rapidly locate missing individuals in dense forested areas. The system will use thermal cameras and machine learning object identification to detect targets, and a mobile charging station will enable uninterrupted searches. This contract is not set aside and is being awarded by a civilian agency, likely the Shared Services Center, to support the university's extensive experience in federal research and development, particularly in areas like aerospace engineering, computational technologies, and addressing societal challenges through interdisciplinary work.

The proposed UAS is based on the quadplane architectural concept, combining multirotor and fixed-wing UAS to provide the best of both vehicles strengths. IT uses the multirotor vertical takeoff and landing capability and fixed-wing efficiency for navigation and cruise flight. This will result in a cost-effective UAS, with a reduced deployment time of 5 minutes, and an enhanced flight duration of 1 hour, and increased efficiency. The main application envisioned with the quadplane is to use IT in search and rescue missions that will aid first responders in rapidly locating missing individuals in dense forested areas. To ensure quick detection and accurate target region localization, we aim to conduct the search operation in two phases. In the first phase, the unmanned aerial systems (UAS) uses a fast-creeping line search and thermal camera detection to rapidly identify the target region of the missing individual(s). Once the target zone is identified, the UAS uses a loiter survey pattern in combination with image-based recognition to precisely locate the identified individual(s). Because trees block the view of conventional cameras, a thermal camera will be utilized instead. Even when using a thermal camera, the heat reflected by branches and tree crowns appears to be similar to that of the human body. To identify individuals in the captured thermal image, a machine learning object identification model will be used. A mobile charging station will be deployed to refill or replace the UAS's battery so the search could continue uninterrupted.