Date of Award
Master of Science
Department of Systems Engineering and Management
John M. Colombi, PhD
Operation of Unmanned Aerial Vehicles (UAV) support many critical missions in the United State Air Force (USAF). Monitoring abnormal behavior is one of many responsibilities of the operator during a mission. Some behaviors are hard to be detect by an operator, especially when flying one or more autonomous vehicles; as such, detections require a high level of attention and focus to flight parameters. In this research, a monitoring system and its algorithm are designed and tested for a target fixed-wing UAV. The Autonomy Monitoring Service (AMS) compares the real vehicle or simulated Vehicle with a similar simulated vehicle using Software in the Loop (SITL).It is hypothesized that the resulting design has the potential to reduce monotonous monitoring, reduce risk of losing vehicles, and increase mission effectiveness. Performance of the prototyped AMS model was examined by several measures, including divergence detection rate, synchronization time, and Upper Control Limit (UCL) of aircraft location variability in different scenarios. Results showed 100 rate of divergence detection out of all divergent events occurred. The weighted mean of AMS synchronization time was 4.02 seconds, and the weighted mean for aircraft location variability was 44.8 meters. The overarching AMS functionality was achieved. AMS supports the concept that humans and machines should be designed to complement each other by sharing responsibilities and behaviors effectively, making final system safer and more reliable.
DTIC Accession Number
Almannaei, Loay Y., "Design and Test of an Autonomy Monitoring Service to Detect Divergent Behaviors on Unmanned Aerial Systems" (2020). Theses and Dissertations. 4057.