Multiobjective Evolution of Biped Robot Gaits Using Advanced Continuous Ant-Colony Optimized Recurrent Neural Networks

This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimize...

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Vydáno v:	IEEE transactions on cybernetics Ročník 48; číslo 6; s. 1910 - 1922
Hlavní autoři:	Juang, Chia-Feng, Yeh, Yen-Ting
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.06.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithm design and analysis Ant colony optimization Ant colony optimization (ACO) biped robot gait control central pattern generators (CPGs) Computer simulation Gait Genetic algorithms Hip Knee Legged locomotion Mathematical programming multiobjective optimization algorithms Multiple objective analysis Neural networks Optimization Pareto optimization Pareto optimum Pitch (inclination) Recurrent neural networks recurrent neural networks (RNNs) Robots Rolling motion Walking Yaw
ISSN:	2168-2267, 2168-2275, 2168-2275
On-line přístup:	Získat plný text
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