3D Path-Following Algorithms for Unmanned Aerial Vehicles Adjusted with Genetic Algorithm

Unmanned Aerial Vehicle (UAV) is a growing research topic due to its wide range of applications. One of its major challenges is the development of the autopilot, responsible for keeping the aircraft in desired flight conditions and for executing navigation tasks. A navigation task that is usually necessary is the path-following, which guarantees that the aircraft follows a predefined trajectory. It is possible to find several approaches for this function, based in geometric and control techniques; however, compared only for the 2D scenario. Therefore, this paper objective is to present new extended path-following algorithms for the 3D scenario, based in the well-known path-following algorithms Lookahead, Non-Linear Guidance Law (NLGL), Pure Pursuit and Line-of-Sight (PLOS) and Vector Field. The algorithms parameters are obtained with Genetic Algorithm optimisation and a comparison between all of them is performed in an environment with and without wind. The results from the simulations show that Vector Field has the best performance and PLOS has the worse one due to a high effort demanded.