Master Thesis: Building an Uncertainty-Robust Reinforcement Learning-based model for UAV self-separation under Uncertainty

Job description

Background The autonomous operation of unmanned aerial vehicles (UAVs) plays an increasingly important role in research and commercial applications. These vehicles can assist with crucial applications, such as emergency response, infrastructure monitoring, and parcel delivery, but are expected to lead to traffic densities too great for human air traffic controllers to handle. Work its ongoing to develop autonomous separation management systems, from planning and trajectory generation to conflict detection and resolution. For conflict detection and resolution (CD&R), Reinforcement Learning (RL) shows great promise, outperforming state-of-the art geometric methods in safety and efficiency under certain conditions. These methods can be shown to be robust to position noise, and especially perform better at high traffic densities. However, most work considers a homogeneous policy: that is, all vehicles employ the same self-separation strategy, which is also the basis for the strong performance shown by the RL models. In realistic operations, low-level airspace is heterogeneous, and will include vehicles such as trauma response helicopters. These trauma helicopters showcase different dyn...