Application of RBFNN for Humanoid robot real time optimal trajectory generation in running

Xusheng Lei; Jianbo Su

doi:10.1007/978-3-540-28648-6_1

Application of RBFNN for Humanoid robot real time optimal trajectory generation in running

Xusheng Lei^*
, Jianbo Su

^*Corresponding author for this work

Shanghai Jiao Tong University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Scopus citations

Abstract

In this paper, a method for trajectory generation in running is proposed with Radial Basis Function Neural Network, which can generate a series of joint trajectories to adjust humanoid robot step length and step time based on the sensor information. Compared with GA, RBFNN use less time to generate new trajectory to deal with sudden obstacles after thorough training. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Fuliang Yin, Chengan Guo, Jun Wang
Publisher	Springer Verlag
Pages	1-6
Number of pages	6
ISBN (Print)	3540228438, 9783540228431
DOIs	https://doi.org/10.1007/978-3-540-28648-6_1
State	Published - 2004
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3174
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-28648-6_1

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Lei, X., & Su, J. (2004). Application of RBFNN for Humanoid robot real time optimal trajectory generation in running. In F. Yin, C. Guo, & J. Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1-6). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3174). Springer Verlag. https://doi.org/10.1007/978-3-540-28648-6_1

Lei, Xusheng ; Su, Jianbo. / Application of RBFNN for Humanoid robot real time optimal trajectory generation in running. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Fuliang Yin ; Chengan Guo ; Jun Wang. Springer Verlag, 2004. pp. 1-6 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Application of RBFNN for Humanoid robot real time optimal trajectory generation in running",

abstract = "In this paper, a method for trajectory generation in running is proposed with Radial Basis Function Neural Network, which can generate a series of joint trajectories to adjust humanoid robot step length and step time based on the sensor information. Compared with GA, RBFNN use less time to generate new trajectory to deal with sudden obstacles after thorough training. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.",

author = "Xusheng Lei and Jianbo Su",

year = "2004",

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language = "英语",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1--6",

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Lei, X & Su, J 2004, Application of RBFNN for Humanoid robot real time optimal trajectory generation in running. in F Yin, C Guo & J Wang (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3174, Springer Verlag, pp. 1-6. https://doi.org/10.1007/978-3-540-28648-6_1

Application of RBFNN for Humanoid robot real time optimal trajectory generation in running. / Lei, Xusheng; Su, Jianbo.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Fuliang Yin; Chengan Guo; Jun Wang. Springer Verlag, 2004. p. 1-6 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3174).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Lei, Xusheng

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AB - In this paper, a method for trajectory generation in running is proposed with Radial Basis Function Neural Network, which can generate a series of joint trajectories to adjust humanoid robot step length and step time based on the sensor information. Compared with GA, RBFNN use less time to generate new trajectory to deal with sudden obstacles after thorough training. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Yin, Fuliang

A2 - Guo, Chengan

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PB - Springer Verlag

ER -

Lei X, Su J. Application of RBFNN for Humanoid robot real time optimal trajectory generation in running. In Yin F, Guo C, Wang J, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 1-6. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-28648-6_1

Application of RBFNN for Humanoid robot real time optimal trajectory generation in running

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