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

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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.
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Document Type: Research Article

Affiliations: 1: Department of Electronics Engineering, Seokyeong University, Jungneung-Dong 16-1, Sungbuk-Gu, Seoul 136-704, South Korea 2: Department of Electrical & Computer Engineering, Michigan State University, East Lansing, MI 48824, USA

Publication date: 01 November 2010

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