Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natu...

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Main Authors:	Chongben Tao, Jie Xue, Zufeng Zhang, Feng Cao, Chunguang Li, Hanwen Gao
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2021-01-01
Series:	Frontiers in Neurorobotics
Subjects:	RoboCup3D humanoid robot PCLPSO parallel distributed layered learning
Online Access:	https://www.frontiersin.org/articles/10.3389/fnbot.2020.600885/full

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record_format	Article
spelling	doaj-b873590a260346c59a94ebb22a03e93b2021-01-15T04:18:46ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182021-01-011410.3389/fnbot.2020.600885600885Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer AlgorithmChongben Tao0Chongben Tao1Jie Xue2Zufeng Zhang3Zufeng Zhang4Zufeng Zhang5Feng Cao6Chunguang Li7Hanwen Gao8School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, ChinaSuzhou Automobile Research Institute, Tsinghua University, Suzhou, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, ChinaDepartment of Automation, Tsinghua University, Beijing, ChinaWuhan Electronic Information Institute, Hubei, ChinaSchool of Computer and Information Technology, Shanxi University, Taiyuan, ChinaSchool of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, ChinaTo improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.https://www.frontiersin.org/articles/10.3389/fnbot.2020.600885/fullRoboCup3Dhumanoid robotPCLPSOparallel distributedlayered learning
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Chongben Tao Chongben Tao Jie Xue Zufeng Zhang Zufeng Zhang Zufeng Zhang Feng Cao Chunguang Li Hanwen Gao
spellingShingle	Chongben Tao Chongben Tao Jie Xue Zufeng Zhang Zufeng Zhang Zufeng Zhang Feng Cao Chunguang Li Hanwen Gao Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm Frontiers in Neurorobotics RoboCup3D humanoid robot PCLPSO parallel distributed layered learning
author_facet	Chongben Tao Chongben Tao Jie Xue Zufeng Zhang Zufeng Zhang Zufeng Zhang Feng Cao Chunguang Li Hanwen Gao
author_sort	Chongben Tao
title	Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm
title_short	Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm
title_full	Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm
title_fullStr	Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm
title_full_unstemmed	Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm
title_sort	gait optimization method for humanoid robots based on parallel comprehensive learning particle swarm optimizer algorithm
publisher	Frontiers Media S.A.
series	Frontiers in Neurorobotics
issn	1662-5218
publishDate	2021-01-01
description	To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.
topic	RoboCup3D humanoid robot PCLPSO parallel distributed layered learning
url	https://www.frontiersin.org/articles/10.3389/fnbot.2020.600885/full
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Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

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