Abstract
This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.
Highlights
This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure
We consider in this article multiple importance sampling [18] that behaves well in the heart of the distribution g, because Monte Carlo and importance sampling samples can be taken into account in the estimation of the density g
The generation of extreme ground impact footprints map has been addressed in this paper for fixed-wing unmanned aerial vehicle (UAV) failure
Summary
We focus on impacts on the ground due to a loss of control of the UAV (unmanned aerial vehicle) after a main engine failure. It is assumed that immediately after the failure, the engine thrust becomes zero and the control surfaces remain stuck in their equilibrium positions. The objective of this section is to present the models and approach that are used to compute the impact points at ground by simulation, based on previous studies by the authors in [7]
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