A maximum entropy moment quadrature method for quasi-real-time flight duration reliability assessment of UAVs

The need for quasi-real-time flight duration reliability awareness of UAVs has increasingly been emphasized in scenarios of emergency communication relay, flood surveys, and wildfire monitoring, etc. This study proposes an efficient reliability assessment framework for the flight duration of fixed-wing UAVs under multi-source uncertainties, allowing for near real-time estimation of the mission risk. The framework integrates a physics model-based flight duration model where the aerodynamic parameters and ambient wind properties are incorporated as uncertain variables. On this basis, the moment quadrature method is employed to obtain the statistical moments of the flight duration, and the method of maximum entropy is proposed to reconstruct the probability density function of the flight duration, enabling fast and accurate reliability assessment. A realistic record-keeping long duration UAV was studied, and assessed with the proposed method with actual meteorological data. Compared to the Monte Carlo method with 106 simulations, the proposed method requires only 96 model evaluations while the maximum absolute error in reliability estimation is about 1%. The results show that the method can minimize the computational demand while maintaining sufficient accuracy, providing a viable approach to near real-time awareness of flight duration reliability.