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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Multiobjective Optimization of a Quadruped Robot Locomotion Using a Genetic Algorithm
verfasst von : Miguel Oliveira, Lino Costa, Ana Rocha, Cristina Santos, Manuel Ferreira
Erschienen in: Soft Computing in Industrial Applications
Verlag: Springer Berlin Heidelberg
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In this work, it is described a gait multiobjective optimization system that allows to obtain fast but stable robot quadruped crawl gaits. We combine bioinspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). A motion architecture based on CPGs oscillators is used to model the locomotion of the robot dog and a GA is used to search parameterizations of the CPGs parameters which minimize the body vibration, maximize the velocity and maximize the wide stability margin. In this problem, there are several conflicting objectives that leads to a multiobjective formulation that is solved using the Weighted Tchebycheff scalarization method. Several experimental results show the effectiveness of this proposed approach.