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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1267732
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