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

• Published in: Advanced robotics 2010-01, Vol.24 (15), p.2199-2214
• Main Authors: Seo, Kisung, Hyun, Soohwan, Goodman, Erik D.
• Format: Article
• Language: English
• Subjects: AUTOMATIC GAIT GENERATION; GENETIC PROGRAMMING; JOINT SPACE TRAJECTORY; QUADRUPED ROBOT
• ISSN: 0169-1864; 1568-5535
• DOI: 10.1163/016918610X534312

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 optimizing 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 optimize only a small, pre-selected set of parameters, but it is difficult to decide which parameters should be included in the optimization to get the best results. Moreover, the number of pre-selected parameters is at least 10, so it can be relatively difficult to optimize 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 optimizes 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.