We propose a trajectory generation algorithm (STGA) that represents realistically and stochastically trajectories followed by unmanned air vehicles (UAVs), in particular quadrotors UAVs. It is meant to be a tool for testing localization, state estimation and control algorithms. We propose to firstly model a number of representative flight scenarios. For each scenario, stochastic trajectories are generated. They follow a parametric non-linear model whose parameters are determined using a multi-objective evolutionary optimization method called particle swarm optimization (PSO). Numerical results are reported to verify feasibility in comparison to pure random unconstrained trajectory algorithm.
