Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm

Chia Feng Juang, Yu Cheng Chang, I. Fang Chung

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

This paper proposes a new recurrent neural network (RNN) structure evolved to control the gait of a hexapod robot for fast forward walking. In this evolutionary robot, the gait control problem is formulated as an optimization problem with the objective of a fast forward walking speed and a small deviation in the forward walking direction. Evolutionary optimization of the RNNs through a group-based hybrid metaheuristic algorithm is proposed to find the optimal RNN controller. Preliminary simulation results with comparisons show the advantage of the proposed approach1.

原文English
主出版物標題GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
發行者Association for Computing Machinery, Inc
頁面111-112
頁數2
ISBN(電子)9781450357647
DOIs
出版狀態Published - 6 7月 2018
事件2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
持續時間: 15 7月 201819 7月 2018

出版系列

名字GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018
國家/地區Japan
城市Kyoto
期間15/07/1819/07/18

指紋

深入研究「Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm」主題。共同形成了獨特的指紋。

引用此