Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Renaud Barate and Antoine Manzanera",
  title =        "Automatic Design of Vision-Based Obstacle Avoidance
                 Controllers Using Genetic Programming",
  year =         "2007",
  volume =       "4926",
  bibdate =      "2008-05-16",
  bibsource =    "DBLP,
  booktitle =    "Artificial Evolution",
  editor =       "Nicolas Monmarch{\'e} and El-Ghazali Talbi and 
                 Pierre Collet and Marc Schoenauer and Evelyne Lutton",
  isbn13 =       "978-3-540-79304-5",
  pages =        "25--36",
  series =       "Lecture Notes in Computer Science",
  address =      "Tours, France",
  month =        oct # " 29-31",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-540-79305-2_3",
  abstract =     "The work presented in this paper is part of the
                 development of a robotic system able to learn context
                 dependent visual clues to navigate in its environment.
                 We focus on the obstacle avoidance problem as it is a
                 necessary function for a mobile robot. As a first step,
                 we use an off-line procedure to automatically design
                 algorithms adapted to the visual context. This
                 procedure is based on genetic programming and the
                 candidate algorithms are evaluated in a simulation
                 environment. The evolutionary process selects
                 meaningful visual primitives in the given context and
                 an adapted strategy to use them. The results show the
                 emergence of several different behaviors outperforming
                 hand-designed controllers.",
  notes =        "EA'07",

Genetic Programming entries for Renaud Barate Antoine Manzanera