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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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