Decision network for interpretable UAV decision making under human supervision

With the increasing number of drones flying simultaneously under the supervision of a limited number of humans, it is essential to delegate more decision-making authority to the autopilot system to minimise intervention. The decision making capabilities of autonomous UAVs have a direct impact on mission safety and reliability. In this paper we propose a decision-making model for UAVs based on decision networks. The non-dominated sorting genetic algorithm (NSGA III) is used to train the model from a provided set of cases. Separate selected cases with the respective expected outcomes were used to test the model, showcasing its capacity to accurately represent cases while maintaining interpretability. We demonstrate that this model can facilitate mission accomplishment with risk minimisation under uncertainty in stochastic environments. In particular, the model provides a meaningful interpretation of the parameters and how they are taken into account when making decisions.