Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/15171

TitleMultiobjective optimization of a quadruped robot locomotion using a genetic algorithm
Author(s)Oliveira, Miguel
Costa, L.
Rocha, Ana Maria A. C.
Santos, Cristina
Ferreira, Manuel João Oliveira
Issue date2011
PublisherSpringer
JournalAdvances in Intelligent and Soft Computing
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/15171
ISBN978-3-642-20504-0
ISSN1867-5662
Publisher versionThe original publication is available at www.springerlink.com
Peer-Reviewedyes
AccessOpen access
Appears in Collections:LES/ALG - Capítulos de livros

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