Genetic programming for real world robot vision

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

@InProceedings{oai:CiteSeerPSU:544306,
  author =       "Martin C. Martin",
  title =        "Genetic programming for real world robot vision",
  booktitle =    "IEEE/RSJ International Conference on Intelligent
                 Robots and System",
  year =         "2002",
  volume =       "1",
  pages =        "67--72",
  address =      "EPFL, Lausanne, Switzerland",
  month =        "30 " # sep # "-5 " # oct,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, collision
                 avoidance, computerised navigation, genetic algorithms,
                 graph grammars, mobile robots, robot vision, autonomous
                 mobile robot, median filter, navigation, obstacle
                 avoidance algorithm, parse trees, real world robot
                 vision, vision algorithms",
  DOI =          "doi:10.1109/IRDS.2002.1041364",
  URL =          "http://www.martincmartin.com/Dissertation/GeneticProgrammingForRealWorldRobotVisionIROS2002Martin.pdf",
  URL =          "http://citeseer.ist.psu.edu/544306.html",
  size =         "6 pages",
  abstract =     "The vision subsystem of an autonomous mobile robot was
                 created using a form of evolutionary computation known
                 as genetic programming. In this form, individuals are
                 algorithms represented as parse trees. The primitives
                 of the representation were specifically chosen to
                 capture the spirit of existing vision algorithms. Thus,
                 the evolutionary computation can be viewed as searching
                 roughly the same space that researchers search when
                 developing their system using trial and error.
                 Traditional image operators such as the Sobel magnitude
                 and a median filter were combined in arbitrary ways,
                 and images from an unmodified office environment were
                 used as training data. A hand written obstacle
                 avoidance algorithm used the output of the best vision
                 algorithm to avoid obstacles in real time. It performed
                 as well as the existing hand written combined
                 navigation and vision systems.",
  notes =        "IROS

                 Artificial Intelligence Lab., MIT, Cambridge, MA,
                 USA

                 ",
}

Genetic Programming entries for Martin C Martin

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