Stochastic optimization by evolutionary methods applied to autonomous aircraft flight control

Created by W.Langdon from gp-bibliography.bib Revision:1.4080

@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",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, GPU",
  URL =          "http://www.polyvionics.com/Thesis.pdf",
  URL =          "http://theses.unistra.fr/ori-oai-search/notice.html?id=2014STRAD031&printable=true",
  URL =          "http://www.theses.fr/2014STRAD031",
  URL =          "http://scdtheses.u-strasbg.fr/ori-oai-search/notice/view/2014STRAD031",
  URL =          "https://publication-theses.unistra.fr/restreint/theses_doctorat/2014/Querry_Stephane_2014_ED269.pdf",
  size =         "198 pages",
  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 aircrafts 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.

                 Interestingly enough, all these different techniques
                 interoperate: GANIAC (Genetic Automatic Nonlinear
                 Identification of Aerodynamic Coefficients) is used in
                 GAFPLAN (Genetic Autonomous Flight Planning and
                 Navigation) that is finally used by GAFCON (Genetic
                 Autonomous Flight Control) for a complete sequence that
                 should make it possible for UAVs to fly in an
                 autonomous way, including learning how to fly with
                 GANIAC.

                 Because these approaches have been designed to be
                 embedded and used in flight, they pave the way to
                 imagining throwing a UAV in the air that would 1) learn
                 how to fly before crashing, then receive a mission by
                 radio. Thanks to the mathematic identification of the
                 platform, the UAV would then be able to find
                 autonomously good trajectories to fly the mission, then
                 good strategies to avoid obstacles or fulfil some
                 flight constraints and finally, autonomously determine
                 the right sequences of commands to actually fly the
                 mission.

                 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
                 (ICube - BFO)

                 ",
}

Genetic Programming entries for Stephane Querry

Citations