Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot

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

@Article{Seo:2010:AR,
  title =        "Genetic Programming-Based Automatic Gait Generation in
                 Joint Space for a Quadruped Robot",
  author =       "Kisung Seo and Soohwan Hyun and Erik D. Goodman",
  journal =      "Advanced Robotics",
  year =         "2010",
  number =       "15",
  volume =       "24",
  pages =        "2199--2214",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0169-1864",
  DOI =          "doi:10.1163/016918610X534312",
  bibdate =      "2010-12-19",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ar/ar24.html#SeoHG10",
  abstract =     "This paper introduces a new approach to developing a
                 fast gait for a quadruped robot using genetic
                 programming (GP). Planning gaits for legged robots is a
                 challenging task that requires optimising parameters in
                 a highly irregular and multi-dimensional space. Several
                 recent approaches have focused on using genetic
                 algorithms (GAs) to generate gaits automatically and
                 have shown significant improvement over previous gait
                 optimization results. Most current GA-based approaches
                 optimise only a small, pre-selected set of parameters,
                 but it is difficult to decide which parameters should
                 be included in the optimisation to get the best
                 results. Moreover, the number of pre-selected
                 parameters is at least 10, so it can be relatively
                 difficult to optimise them, given their high degree of
                 interdependence. To overcome these problems of the
                 typical GA-based approach, we have proposed a seemingly
                 more efficient approach that optimises joint
                 trajectories instead of locus-related parameters in
                 Cartesian space, using GP. Our GP-based method has
                 obtained much-improved results over the GA-based
                 approaches tested in experiments on the Sony AIBO ERS-7
                 in the Webots environment. The elite archive mechanism
                 is introduced to combat the premature convergence
                 problems in GP and has shown better results than a
                 traditional multi-population approach.",
}

Genetic Programming entries for Kisung Seo Soohwan Hyun Erik Goodman

Citations