Risk-Aware Markov Decision Process Contingency Management Autonomy for Uncrewed Aircraft Systems

Abstract

Uncrewed aircraft systems (UASs) are being increasingly adopted for a variety of applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov decision process (MDP), whose solution is a contingency management policy that appropriately executes emergency landing, flight termination, or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, the partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP and maximum a posteriori MDP policies had similar performance over different state observability criteria, given the nearly deterministic state transition model.