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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00207207%21RIV14-MSM-21230___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
Recently, a new co-evolutionary approach for generating motion patterns for multi-legged robots which exhibit symmetry and module repetition was proposed. The algorithm consists of two evolutionary algorithms working in co-evolution. The first one, a genetic programming module, evolves a motion of a single leg. The second one, a genetic algorithm module, seeks for the optimal deployment of the single-leg motion pattern to all legs of the robot. Thus, the whole task is decomposed into two subtasks that are to be solid simultaneously. First proof-of-concept experiments proved such a decomposition helps to produce better solutions than a simple GP-based approach that tries to evolve individual motion patterns for all legs of the robot. This paper further analyses the co-evolutionary algorithm focusing on two things – the way it handles the problem decomposition and the type of functions it uses to control joints of the robot. The experiments carried out in this work indicate that both design choices positively contribute to its performance. Recently, a new co-evolutionary approach for generating motion patterns for multi-legged robots which exhibit symmetry and module repetition was proposed. The algorithm consists of two evolutionary algorithms working in co-evolution. The first one, a genetic programming module, evolves a motion of a single leg. The second one, a genetic algorithm module, seeks for the optimal deployment of the single-leg motion pattern to all legs of the robot. Thus, the whole task is decomposed into two subtasks that are to be solid simultaneously. First proof-of-concept experiments proved such a decomposition helps to produce better solutions than a simple GP-based approach that tries to evolve individual motion patterns for all legs of the robot. This paper further analyses the co-evolutionary algorithm focusing on two things – the way it handles the problem decomposition and the type of functions it uses to control joints of the robot. The experiments carried out in this work indicate that both design choices positively contribute to its performance.
dcterms:title
Analysis of Co-evolutionary Approach for Robotic Gait Generation Analysis of Co-evolutionary Approach for Robotic Gait Generation
skos:prefLabel
Analysis of Co-evolutionary Approach for Robotic Gait Generation Analysis of Co-evolutionary Approach for Robotic Gait Generation
skos:notation
RIV/68407700:21230/13:00207207!RIV14-MSM-21230___
n8:predkladatel
n9:orjk%3A21230
n3:aktivita
n5:S n5:Z
n3:aktivity
S, Z(MSM6840770038)
n3:dodaniDat
n20:2014
n3:domaciTvurceVysledku
n16:1775022 n16:2993457
n3:druhVysledku
n12:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
60495
n3:idVysledku
RIV/68407700:21230/13:00207207
n3:jazykVysledku
n14:eng
n3:klicovaSlova
Evolutionary Algorithms; Genetic Programming; Legged Robots; Robotic Gait; Co-evolution; Motion Patterns; Evolutionary Robotics
n3:klicoveSlovo
n4:Motion%20Patterns n4:Legged%20Robots n4:Genetic%20Programming n4:Evolutionary%20Robotics n4:Co-evolution n4:Robotic%20Gait n4:Evolutionary%20Algorithms
n3:kontrolniKodProRIV
[579A82FA3C81]
n3:mistoKonaniAkce
Algarve
n3:mistoVydani
Porto
n3:nazevZdroje
Proceedings of the 5th International Joint Conference on Computational Intelligence
n3:obor
n13:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n20:2013
n3:tvurceVysledku
Černý, Jan Kubalík, Jiří
n3:typAkce
n21:WRD
n3:zahajeniAkce
2013-09-20+02:00
n3:zamer
n17:MSM6840770038
s:numberOfPages
10
n7:hasPublisher
SciTePress - Science and Technology Publications
n22:isbn
978-989-8565-77-8
n15:organizacniJednotka
21230