Abstract
Neural Oscillators are network of neurons that produce smooth oscillatory/rhythmic pattern. In this paper, a simple network of Matsuoka oscillators is used to generate the bipedal locomotion by coordinating the joint angle using a new method of coordination proposed by Huang. This method makes the control of the joint angle easier as they are related with each other in a sequential manner. Also, this method prevents error accumulation with time. The results show that the robot produced a smooth walking motion.
| Original language | English |
|---|---|
| Title of host publication | Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings |
| Pages | 304-312 |
| Number of pages | 9 |
| Volume | 7552 LNCS |
| Edition | PART 1 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland Duration: Sep 11 2012 → Sep 14 2012 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 1 |
| Volume | 7552 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 22nd International Conference on Artificial Neural Networks, ICANN 2012 |
|---|---|
| Country | Switzerland |
| City | Lausanne |
| Period | 9/11/12 → 9/14/12 |
Fingerprint
Keywords
- bipedal robot
- CPG
- locomotion
- Matsuoka oscillator
- neural oscillator
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Control of biped robot joints' angles using coordinated Matsuoka oscillators. / Al-Busaidi, Asiya M.; Zaier, Riadh; Al-Yahmadi, Amer S.
Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. Vol. 7552 LNCS PART 1. ed. 2012. p. 304-312 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7552 LNCS, No. PART 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Control of biped robot joints' angles using coordinated Matsuoka oscillators
AU - Al-Busaidi, Asiya M.
AU - Zaier, Riadh
AU - Al-Yahmadi, Amer S.
PY - 2012
Y1 - 2012
N2 - Neural Oscillators are network of neurons that produce smooth oscillatory/rhythmic pattern. In this paper, a simple network of Matsuoka oscillators is used to generate the bipedal locomotion by coordinating the joint angle using a new method of coordination proposed by Huang. This method makes the control of the joint angle easier as they are related with each other in a sequential manner. Also, this method prevents error accumulation with time. The results show that the robot produced a smooth walking motion.
AB - Neural Oscillators are network of neurons that produce smooth oscillatory/rhythmic pattern. In this paper, a simple network of Matsuoka oscillators is used to generate the bipedal locomotion by coordinating the joint angle using a new method of coordination proposed by Huang. This method makes the control of the joint angle easier as they are related with each other in a sequential manner. Also, this method prevents error accumulation with time. The results show that the robot produced a smooth walking motion.
KW - bipedal robot
KW - CPG
KW - locomotion
KW - Matsuoka oscillator
KW - neural oscillator
UR - http://www.scopus.com/inward/record.url?scp=84867669220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867669220&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33269-2_39
DO - 10.1007/978-3-642-33269-2_39
M3 - Conference contribution
SN - 9783642332685
VL - 7552 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 304
EP - 312
BT - Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
ER -