Autonomous evolution of high-speed quadruped gaits using particle swarm optimization

Chunxia Rong; Qining Wang; Yan Huang; Guangming Xie; Long Wang

doi:10.1007/978-3-642-02921-9_23

Autonomous evolution of high-speed quadruped gaits using particle swarm optimization

Chunxia Rong^*, Qining Wang, Yan Huang, Guangming Xie, Long Wang

^*Corresponding author for this work

Peking University

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

4 Citations (Scopus)

Abstract

This paper presents a novel evolutionary computation approach to optimize fast forward gaits for a quadruped robot with three motor-driven joints on each limb. Our learning approach uses Particle Swarm Optimization to search for a set of parameters automatically aiming to develop the fastest gait that an actual quadruped robot can possibly achieve, based on the concept of parameterized representation for quadruped gaits. In addition, we analyze the computational cost of Particle Swarm Optimization taking the memory requirements and processing limitation into consideration. Real robot experiments show that the evolutionary approach is effective in developing quadruped gaits. Satisfactory results are obtained in less than an hour by the autonomous learning process, which starts with randomly generated parameters instead of any hand-tuned parameters.

Original language	English
Title of host publication	RoboCup 2008
Subtitle of host publication	Robot Soccer World Cup XII
Pages	259-270
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-02921-9_23
Publication status	Published - 2009
Externally published	Yes
Event	12th annual RoboCup International Symposium, RoboCup 2008 - Suzhou, China Duration: 15 Jul 2008 → 18 Jul 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5399 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th annual RoboCup International Symposium, RoboCup 2008
Country/Territory	China
City	Suzhou
Period	15/07/08 → 18/07/08

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10.1007/978-3-642-02921-9_23

Cite this

Rong, C., Wang, Q., Huang, Y., Xie, G., & Wang, L. (2009). Autonomous evolution of high-speed quadruped gaits using particle swarm optimization. In RoboCup 2008: Robot Soccer World Cup XII (pp. 259-270). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5399 LNAI). https://doi.org/10.1007/978-3-642-02921-9_23

@inproceedings{1e7a7009fbfd489b82d1aaaafbf1067c,

title = "Autonomous evolution of high-speed quadruped gaits using particle swarm optimization",

abstract = "This paper presents a novel evolutionary computation approach to optimize fast forward gaits for a quadruped robot with three motor-driven joints on each limb. Our learning approach uses Particle Swarm Optimization to search for a set of parameters automatically aiming to develop the fastest gait that an actual quadruped robot can possibly achieve, based on the concept of parameterized representation for quadruped gaits. In addition, we analyze the computational cost of Particle Swarm Optimization taking the memory requirements and processing limitation into consideration. Real robot experiments show that the evolutionary approach is effective in developing quadruped gaits. Satisfactory results are obtained in less than an hour by the autonomous learning process, which starts with randomly generated parameters instead of any hand-tuned parameters.",

author = "Chunxia Rong and Qining Wang and Yan Huang and Guangming Xie and Long Wang",

year = "2009",

doi = "10.1007/978-3-642-02921-9_23",

language = "English",

isbn = "3642029205",

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

pages = "259--270",

booktitle = "RoboCup 2008",

note = "12th annual RoboCup International Symposium, RoboCup 2008 ; Conference date: 15-07-2008 Through 18-07-2008",

}

Rong, C, Wang, Q, Huang, Y, Xie, G & Wang, L 2009, Autonomous evolution of high-speed quadruped gaits using particle swarm optimization. in RoboCup 2008: Robot Soccer World Cup XII. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5399 LNAI, pp. 259-270, 12th annual RoboCup International Symposium, RoboCup 2008, Suzhou, China, 15/07/08. https://doi.org/10.1007/978-3-642-02921-9_23

Autonomous evolution of high-speed quadruped gaits using particle swarm optimization. / Rong, Chunxia; Wang, Qining; Huang, Yan et al.
RoboCup 2008: Robot Soccer World Cup XII. 2009. p. 259-270 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5399 LNAI).

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

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AU - Wang, Qining

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AB - This paper presents a novel evolutionary computation approach to optimize fast forward gaits for a quadruped robot with three motor-driven joints on each limb. Our learning approach uses Particle Swarm Optimization to search for a set of parameters automatically aiming to develop the fastest gait that an actual quadruped robot can possibly achieve, based on the concept of parameterized representation for quadruped gaits. In addition, we analyze the computational cost of Particle Swarm Optimization taking the memory requirements and processing limitation into consideration. Real robot experiments show that the evolutionary approach is effective in developing quadruped gaits. Satisfactory results are obtained in less than an hour by the autonomous learning process, which starts with randomly generated parameters instead of any hand-tuned parameters.

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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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Autonomous evolution of high-speed quadruped gaits using particle swarm optimization

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