Robust Machine Learning for Unmanned Traffic Management Systems – ROMEO

ROMEO is a project which aims to develop a robust system for anomaly detection in drone trajectories based on the use of physics informed neural networks, statistical methods for anomaly detection and quantification of uncertainty through conformal inference methods. In this consortium Thales will bring its expertise in the use of physics informed neural networks and anomaly detection. The "Institut de Mathématiques de Toulouse" (IMT) will bring its expertise in uncertainty evaluation through conformal inference.
The massive usage of drones open the path to multiple applications both civil and for defense, including surveillance or smart logistic missions. Such applications may require to use large numbers of drones and in this context, it is crucial to ensure a safe and secure usage of drones through unmanned traffic management (UTM) systems solutions that are both efficient and reliable. In this project, we propose a system which raises alerts for UTM operators. This system raise an alert when an anomaly is detected in the drone trajectory when compared with expected trajectory. The prediction of the normal trajectory will be based on physics informed neural networks, allowing to introduce prior knowledge on the flight dynamics. The anomaly detection will be performed with innovative statistical metrics. The uncertainty on the normal trajectory prediction will be estimated with conformal inference methods. This uncertainty bounds will be integrated in the anomaly detection method in order to provide the operator with trustable alarms.