Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming

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

@InProceedings{barlow2004-cis,
  author =       "Gregory J. Barlow and Choong K. Oh and Edward Grant",
  title =        "Incremental Evolution of Autonomous Controllers for
                 Unmanned Aerial Vehicles using Multi-objective Genetic
                 Programming",
  booktitle =    "Proceedings of the 2004 IEEE Conference on Cybernetics
                 and Intelligent Systems (CIS)",
  year =         "2004",
  pages =        "688--693",
  address =      "Singapore",
  month =        "1-3 " # dec,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, incremental
                 evolution, multi-objective optimisation",
  URL =          "http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2004-cis/barlow2004-cis.pdf",
  abstract =     "Autonomous navigation controllers were developed for
                 fixed wing unmanned aerial vehicle (UAV) applications
                 using incremental evolution with multi-objective
                 genetic programming (GP). We designed four fitness
                 functions derived from flight simulations and used
                 multi-objective GP to evolve controllers able to locate
                 a radar source, navigate the UAV to the source
                 efficiently using on-board sensor measurements, and
                 circle closely around the emitter. We selected
                 realistic flight parameters and sensor inputs to aid in
                 the transference of evolved controllers to physical
                 UAVs. We used both direct and environmental incremental
                 evolution to evolve controllers for four types of
                 radars: 1) continuously emitting, stationary radars, 2)
                 continuously emitting, mobile radars, 3) intermittently
                 emitting, stationary radars, and 4) intermittently
                 emitting, mobile radars. The use of incremental
                 evolution drastically increased evolution's chances of
                 evolving a successful controller compared to direct
                 evolution. This technique can also be used to develop a
                 single controller capable of handling all four radar
                 types. In the next stage of research, the best evolved
                 controllers will be tested by using them to fly real
                 UAVs.",
  notes =        "IEEE CIS RAM 2004 http://cis-ram.nus.edu.sg/",
}

Genetic Programming entries for Gregory J Barlow Choong K Oh Edward Grant

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