Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{barlow:2004:geccogsw,

• author = "Gregory J. Barlow",
• title = "Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming",
• booktitle = "Proceedings of the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004)",
• editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela",
• year = "2004",
• address = "Seattle, Washington, USA",
• month = "24-26 " # jun,
• keywords = "genetic algorithms, genetic programming, evolutionary robotics, multi-objective optimisation, unmanned aerial vehicles",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WGSW001.pdf",
• URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/conference/barlow2004-geccogsw/barlow2004-geccogsw.pdf",
• abstract = "Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using multi-objective genetic programming (GP). Four fitness functions derived from flight simulations were designed and multi-objective GP was used to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. Controllers were evolved for three different kinds of radars: stationary, continuously emitting radars, stationary, intermittently emitting radars, and mobile, continuously emitting radars. In this study, realistic flight parameters and sensor inputs were selected to aid in the transference of evolved controllers to physical UAVs.",
• notes = "Winner of Best Paper at the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004). http://www-illigal.ge.uiuc.edu:8080/GECCO-2004/awards-winners.html

  GECCO-2004WKS Distributed on CD-ROM at GECCO-2004",

}

Genetic Programming entries for Gregory J Barlow

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