Stochastic optimization by evolutionary methods applied to autonomous aircraft flight control

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Querry:thesis,
  author =       "Stephane Querry",
  title =        "Stochastic optimization by evolutionary methods
                 applied to autonomous aircraft flight control",
  school =       "Laboratoire des sciences de l'ingenieur, de
                 l'informatique et de l'imagerie (Strasbourg)",
  year =         "2014",
  address =      "France",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://theses.unistra.fr/ori-oai-search/notice.html?id=2014STRAD031&printable=true",
  URL =          "http://www.theses.fr/2014STRAD031",
  abstract =     "The object of this PhD has consisted in elaborating
                 evolutionary computing algorithms to find interesting
                 solutions to important problems in several domains of
                 automation science, applied to aircraft's mission
                 conduction and to understand what could be the
                 advantages of using such approaches, compared to the
                 state-of-the-art, in terms of efficiency, robustness,
                 and effort of implementation.New algorithms have been
                 developed, in Identification, Path planning, Navigation
                 and Control and have been tested on simulation and on
                 real world platforms (AR.Drone 3.0 UAV (Parrot),
                 Oktokopter UAV, Twin Otter and military fighter F-16
                 (NASA LaRC)), to assess the performances improvements,
                 given by the new proposed approaches. Most of these new
                 approaches provide very interesting results; and
                 research work (on control by evolutionary algorithms,
                 identification by genetic programming and relative
                 navigation) should be engaged to plan potential
                 applications in different real world technologies.",
  resume =       "Le but de ce doctorat est de determiner dans quelle
                 mesure les algorithmes issus de l'intelligence
                 artificielle, principalement les Algorithmes
                 Evolutionnaires et la Programmation Genetique,
                 pourraient aider les algorithmes de l'automatique
                 classique afin de permettre aux engins autonomes de
                 disposer de capacites bien superieures, et ce dans les
                 domaines de l'identification, de la planification de
                 trajectoire, du pilotage et de la navigation.De
                 nouveaux algorithmes ont ete developpes, dans les
                 domaines de l'identification, de la planification de
                 trajectoire, de la navigation et du controle, et ont
                 ete testes sur des systemes de simulation et des
                 aeronefs du monde reel (Oktokopter du ST2I, Bebop.Drone
                 de la societe Parrot, Twin Otter et F-16 de la NASA) de
                 maniere a evaluer les apports de ces nouvelles
                 approches par rapport a l'etat de l'art.La plupart de
                 ces nouvelles approches ont permis d'obtenir de tres
                 bons resultats compares a l'etat de l'art, notamment
                 dans le domaine de l'identification et de la commande,
                 et un approfondissement des travaux devraient etre
                 engage afin de developper le potentiel applicatifs de
                 certains algorithmes.",
  notes =        "anglais 2014STRAD031

                 Supervisor Pierre Collet
                 http://www.idref.fr/162130066

                 Le texte integral de cette these sera accessible sur
                 intranet a partir du 01-10-2017",
}

Genetic Programming entries for Stephane Querry

Citations