Efficient Generation of Ground Impact Probability Maps by Neural Networks for Risk Analysis of UAV Missions

This paper investigates the generation of ground impact probability maps for UAVs in case of failure during the flight. Such maps are of a huge interest for risk assessment of UAV operations and can be used both for offline mission preparation or analysis and online decision making. Two approaches are proposed in this paper to generate such maps, taking into account a dynamical model a fixed-wing UAV and wind conditions. The first one relies on the generation of a complete database by Monte Carlo simulations. The second one is based on neural network surrogate models obtained by supervised learning using this database. Computation time required by the second approach is very small and compatible with online use. The two approaches are presented and discussed, and examples of ground impact probability maps generated are provided.