Genetic Programming for Robot Vision

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  title =        "Genetic Programming for Robot Vision",
  author =       "Martin C. Martin",
  year =         "2002",
  booktitle =    "The Seventh International Conference on the Simulation
                 of Adaptive Behavior (SAB'02)",
  editor =       "Bridget Hallam and Dario Floreano",
  address =      "Edinburgh, UK",
  month =        "9-11 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  citeseer-isreferencedby = "oai:CiteSeerPSU:90959",
  citeseer-references = "oai:CiteSeerPSU:218933; oai:CiteSeerPSU:57892;
                 oai:CiteSeerPSU:23206; oai:CiteSeerPSU:124886;
                 oai:CiteSeerPSU:50260; oai:CiteSeerPSU:212034;
                 oai:CiteSeerPSU:368283; oai:CiteSeerPSU:544929;
                 oai:CiteSeerPSU:40597; oai:CiteSeerPSU:14506;
                 oai:CiteSeerPSU:72759; oai:CiteSeerPSU:294737;
                 oai:CiteSeerPSU:26627; oai:CiteSeerPSU:295170",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:547772",
  rights =       "unrestricted",
  URL =          "",
  URL =          "",
  abstract =     "Genetic Programming was used to create the vision
                 subsystem of a reactive obstacle avoidance system for
                 an autonomous mobile robot. The representation of
                 algorithms was specifically chosen to capture the
                 spirit of existing, hand written vision algorithms.
                 Traditional computer vision operators such as Sobel
                 gradient magnitude, median filters and the Moravec
                 interest operator were combined arbitrarily. Images
                 from an office hallway were used as training data. The
                 evolved programs took a black and white camera image as
                 input and estimated the location of the lowest
                 non-ground pixel in a given column. The computed
                 estimates were then given to a handwritten obstacle
                 avoidance algorithm and used to control the robot in
                 real time. Evolved programs successfully navigated in
                 unstructured hallways, performing on par with
                 hand-crafted systems.",
  notes =        "",

Genetic Programming entries for Martin C Martin