Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty

CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.