Monte Carlo simulation for vision-based autonomous landing of unmanned combat aerial vehicles

Application of uncertainty analysis methods was introduced as well as some general used uncertainty models. The most basic uncertainty analysis method is Monte Carlo Simulation, which is a powerful and practical tool to deal with non-linear systems with very large amount of uncertainties. However, the great disadvantage of Monte Carlo is that it is very intensive computationally. Given to such a drawback, a distributed computing tool, which is named Monte Carlo Simulation Tool and based on MATLAB Distributed Computing Engine and Distributed Computing Toolbox, was developed in order to execute independent MATLAB operations simultaneously on a cluster of computers, speeding up execution of large amount of simulations. The MCST can easily be used to Simulink models for Monte Carlo Simulation. Monte Carlo Simulation has been applied to vision-based autonomous landing of Unmanned Combat Aerial Vehicles (UCAVs) with uncertainties of initial conditions and sensor measurements. Simulation results show that there is a high mission successful probability, which means the autonomous landing control law is insensitive to uncertainties.