Keith Mottas is the CEO of Atlantic Drone Pros, LLC, an unmanned aircraft systems (UAS) technology company. With over 35 years of experience in aviation, Mottas has flown both single- and multi-engine manned aircraft, accumulating over 3200 pilot-in-command (PIC) hours. He has also flown 650 PIC hours of the MQ-9 on combat missions and over 200 hours of UASs/UAVs. Mottas is a Federal Aviation Administration (FAA)-certified flight instructor and a representative of the FAA Safety Team. He brings extensive knowledge and experience in developing programs for UAS/UAV/RPA equipment and flight crews, commercial operations, emergency services, law enforcement, EMS, and search and rescue.

Mottas studied Aeronautics/Aviation/Aerospace Science and Technology, General at Liberty University, earning a Bachelor of Science with a grade point average (GPA) of 3.91. He holds a Department of Defense (DoD) security clearance, and his team members also have the appropriate clearance for any project.

Atlantic Drone Pros was recently awarded an STTR through Agility Prime/AFWERX, where they will develop artificial intelligence (AI) and machine learning (ML) for weather and traffic avoidance in UAS. Mottas and his team are partnered with the University of North Dakota, Daniel Wagner Associates, and TruWeather in this endeavor. They are also developing a trajectory-based operation (TBO) infrastructure in a NASA SBIR, and a ground control system (GCS) for a single pilot to operate ten or more UASs through AI in another.

Mottas and his team have integrated various technology patents licensed from NASA into an end-to-end flight and fleet management system called FLITE Core™. This system is expected to be the next major breakthrough in fully autonomous intelligence that can take the Pilot Out of the Loop (POOL™). Consequently, the disruptive technology will be licensed to aircraft manufacturers, and numerous software applications will be loaded onto UASs.

Furthermore, Atlantic Drone Pros is working to enhance several patents with NASA, develop licensable software programs and sensors loaded onto processors for integration onto UASs that will identify human, vehicles, buildings and obstacles below and near it using computer vision to determine whether it is safe to land. The aircraft will also detect and avoid other air traffic while in autonomous flight through the sense and avoid computer vision with AI and ML.