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.
A modelling approach to generate representative UAV trajectories using PSO
Tonello A. M.
2019-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
