Fault-tolerant SCADA for UAVs In Inaccessible Environments

Remote locations including but not limited to, far-reaching offshore airspaces, provide limited communications capabilities between equipment and ground stations. The case for DAVs is no exception, all the while requiring swift decisions because of the high speeds, lack of detailed real-time weather data, and complex applications. With the limitations and require-ments at hand, we propose real-time edge fault-tolerant SCADA (detection of anomalies) based on learning continuous sequential data for efficient control of the UAV's altitude and heading. Our data-driven solution, involving an extensive suite of sensory data processing, demonstrates the potential to significantly reduce communication and decision-making capabilities in cases of remote locations, enabling safer and more efficient UAV operations. The proposed system leverages machine learning algorithms to analyze real-time data from both extrinsic and intrinsic UAV sensors, allowing for predictive control and fault detection. By processing data at the edge, our solution reduces the need for bandwidth-intensive data transmission minimizing latency and ensuring swift and reliable decision-making. We show the data collection and training in a high-fidelity aviation simulator that closely matches real flight conditions.