Running trajectory generation for humanoid robot

Xu Sheng Lei; Jian Bo Su

Running trajectory generation for humanoid robot

Xu Sheng Lei^*
, Jian Bo Su

^*Corresponding author for this work

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

4 Scopus citations

Abstract

In this paper, using minimum energy function as an optimization tool, a stable trajectory for a humanoid robot to run is achieved with the genetic algorithm. Compared with other methods, it can reduce consumed energy during the whole process. The proposed method is adaptive to various step lengths and step times for running and could be applied in other missions, such as walking, overcoming obstacles. The system stability is improved by controlling shoulders and elbows to compensate the errors. 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	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages	1002-1007
Number of pages	6
State	Published - 2004
Externally published	Yes
Event	Proceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: 26 Aug 2004 → 29 Aug 2004

Publication series

Name	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume	2

Conference

Conference	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Shanghai
Period	26/08/04 → 29/08/04

Keywords

GA
Humanoid robot
Optimal trajectory

Cite this

@inproceedings{c486036bc03649d7bbd91750cdf81247,

title = "Running trajectory generation for humanoid robot",

abstract = "In this paper, using minimum energy function as an optimization tool, a stable trajectory for a humanoid robot to run is achieved with the genetic algorithm. Compared with other methods, it can reduce consumed energy during the whole process. The proposed method is adaptive to various step lengths and step times for running and could be applied in other missions, such as walking, overcoming obstacles. The system stability is improved by controlling shoulders and elbows to compensate the errors. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.",

keywords = "GA, Humanoid robot, Optimal trajectory",

author = "Lei, \{Xu Sheng\} and Su, \{Jian Bo\}",

year = "2004",

language = "英语",

isbn = "0780384032",

series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

pages = "1002--1007",

booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

note = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

}

Lei, XS & Su, JB 2004, Running trajectory generation for humanoid robot. in Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1002-1007, Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26/08/04.

Running trajectory generation for humanoid robot. / Lei, Xu Sheng; Su, Jian Bo.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics. 2004. p. 1002-1007 (Proceedings of 2004 International Conference on Machine Learning and Cybernetics; Vol. 2).

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

TY - GEN

T1 - Running trajectory generation for humanoid robot

AU - Lei, Xu Sheng

AU - Su, Jian Bo

PY - 2004

Y1 - 2004

N2 - In this paper, using minimum energy function as an optimization tool, a stable trajectory for a humanoid robot to run is achieved with the genetic algorithm. Compared with other methods, it can reduce consumed energy during the whole process. The proposed method is adaptive to various step lengths and step times for running and could be applied in other missions, such as walking, overcoming obstacles. The system stability is improved by controlling shoulders and elbows to compensate the errors. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.

AB - In this paper, using minimum energy function as an optimization tool, a stable trajectory for a humanoid robot to run is achieved with the genetic algorithm. Compared with other methods, it can reduce consumed energy during the whole process. The proposed method is adaptive to various step lengths and step times for running and could be applied in other missions, such as walking, overcoming obstacles. The system stability is improved by controlling shoulders and elbows to compensate the errors. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.

KW - GA

KW - Humanoid robot

KW - Optimal trajectory

UR - https://www.scopus.com/pages/publications/6344254738

M3 - 会议稿件

AN - SCOPUS:6344254738

SN - 0780384032

T3 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

SP - 1002

EP - 1007

BT - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

T2 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

Running trajectory generation for humanoid robot

Abstract

Publication series

Conference

Keywords

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