Learning Vision Algorithms for Real Mobile Robots with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Renaud Barate and Antoine Manzanera",
  title =        "Learning Vision Algorithms for Real Mobile Robots with
                 Genetic Programming",
  booktitle =    "ECSIS Symposium on Learning and Adaptive Behaviors for
                 Robotic Systems, LAB-RS '08",
  year =         "2008",
  month =        aug,
  pages =        "47--52",
  keywords =     "genetic algorithms, genetic programming, learning
                 vision algorithms, mobile robots, obstacle avoidance
                 algorithms, supervised learning system, control
                 engineering computing, learning (artificial
                 intelligence), mobile robots, robot vision",
  DOI =          "doi:10.1109/LAB-RS.2008.20",
  abstract =     "We present a genetic programming system to evolve
                 vision based obstacle avoidance algorithms. In order to
                 develop autonomous behavior in a mobile robot, our
                 purpose is to design automatically an obstacle
                 avoidance controller adapted to the current context. We
                 first record short sequences where we manually guide
                 the robot to move away from the walls. This set of
                 recorded video images and commands is our learning
                 base. Genetic programming is used as a supervised
                 learning system to generate algorithms that exhibit
                 this corridor centering behavior. We show that the
                 generated algorithms are efficient in the corridor that
                 was used to build the learning base, and that they
                 generalize to some extent when the robot is placed in a
                 visually different corridor. More, the evolution
                 process has produced algorithms that go past a
                 limitation of our system, that is the lack of adequate
                 edge extraction primitives. This is a good indication
                 of the ability of this method to find efficient
                 solutions for different kinds of environments.",
  notes =        "Also known as \cite{4599426}",

Genetic Programming entries for Renaud Barate Antoine Manzanera