Created by W.Langdon from gp-bibliography.bib Revision:1.4759
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.",
Supervisor Pierre Collet http://www.idref.fr/162130066 (ICube - BFO)
Genetic Programming entries for Stephane Querry