Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot

Azhar Aulia Saputra; Indra Adji Sulistijono; Naoyuki Kubota

doi:10.1007/978-3-319-43506-0_52

Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot

Azhar Aulia Saputra^*
, Indra Adji Sulistijono
, Naoyuki Kubota

^*Corresponding author for this work

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

1 Scopus citations

Abstract

Biologically inspired control system is necessary to be increased. This paper proposed the new design of multimodal neural network inspired from human learning system which takes different action in different condition. The multimodal neural network consists of some recurrent neural networks (RNNs) those are separated into different condition. There is selector system that decides certain RNN system depending the current condition of the robot. In this paper, we implemented this system in pendulum mobile robot as the basic object of study. Several certain number of RNNs are implemented into certain different condition of tilt robot. RNN works alternately depending on the condition of robot. In order to prove the effectiveness of the proposed model, we simulated in the computer simulation Open Dynamic Engine (ODE) and compared with ordinary RNN. The proposed neural model successfully stabilize the applied robot (2-wheeled robot). This model is developed for implemented into humanoid balancing learning system as the final object of study.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings
Editors	Honghai Liu, Naoyuki Kubota, Takenori Obo, Kazuo Kiguchi
Publisher	Springer Verlag
Pages	596-608
Number of pages	13
ISBN (Print)	9783319435053
DOIs	https://doi.org/10.1007/978-3-319-43506-0_52
State	Published - 2016
Externally published	Yes
Event	9th International Conference on Intelligent Robotics and Applications, ICIRA 2016 - Tokyo, Japan Duration: 22 Aug 2016 → 24 Aug 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9834 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Intelligent Robotics and Applications, ICIRA 2016
Country/Territory	Japan
City	Tokyo
Period	22/08/16 → 24/08/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

MRNN
RNN
Two-wheeled robot

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-43506-0_52

Cite this

Saputra, A. A., Sulistijono, I. A., & Kubota, N. (2016). Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot. In H. Liu, N. Kubota, T. Obo, & K. Kiguchi (Eds.), Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings (pp. 596-608). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9834 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-43506-0_52

Saputra, Azhar Aulia ; Sulistijono, Indra Adji ; Kubota, Naoyuki. / Multimodal recurrent neural network (MRNN) based self balancing system : Applied into two-wheeled robot. Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings. editor / Honghai Liu ; Naoyuki Kubota ; Takenori Obo ; Kazuo Kiguchi. Springer Verlag, 2016. pp. 596-608 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot",

abstract = "Biologically inspired control system is necessary to be increased. This paper proposed the new design of multimodal neural network inspired from human learning system which takes different action in different condition. The multimodal neural network consists of some recurrent neural networks (RNNs) those are separated into different condition. There is selector system that decides certain RNN system depending the current condition of the robot. In this paper, we implemented this system in pendulum mobile robot as the basic object of study. Several certain number of RNNs are implemented into certain different condition of tilt robot. RNN works alternately depending on the condition of robot. In order to prove the effectiveness of the proposed model, we simulated in the computer simulation Open Dynamic Engine (ODE) and compared with ordinary RNN. The proposed neural model successfully stabilize the applied robot (2-wheeled robot). This model is developed for implemented into humanoid balancing learning system as the final object of study.",

keywords = "MRNN, RNN, Two-wheeled robot",

author = "Saputra, \{Azhar Aulia\} and Sulistijono, \{Indra Adji\} and Naoyuki Kubota",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 9th International Conference on Intelligent Robotics and Applications, ICIRA 2016 ; Conference date: 22-08-2016 Through 24-08-2016",

year = "2016",

doi = "10.1007/978-3-319-43506-0\_52",

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Saputra, AA, Sulistijono, IA & Kubota, N 2016, Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot. in H Liu, N Kubota, T Obo & K Kiguchi (eds), Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9834 LNCS, Springer Verlag, pp. 596-608, 9th International Conference on Intelligent Robotics and Applications, ICIRA 2016, Tokyo, Japan, 22/08/16. https://doi.org/10.1007/978-3-319-43506-0_52

Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot. / Saputra, Azhar Aulia; Sulistijono, Indra Adji; Kubota, Naoyuki.
Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings. ed. / Honghai Liu; Naoyuki Kubota; Takenori Obo; Kazuo Kiguchi. Springer Verlag, 2016. p. 596-608 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9834 LNCS).

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

TY - GEN

T1 - Multimodal recurrent neural network (MRNN) based self balancing system

T2 - 9th International Conference on Intelligent Robotics and Applications, ICIRA 2016

AU - Saputra, Azhar Aulia

AU - Sulistijono, Indra Adji

AU - Kubota, Naoyuki

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - Biologically inspired control system is necessary to be increased. This paper proposed the new design of multimodal neural network inspired from human learning system which takes different action in different condition. The multimodal neural network consists of some recurrent neural networks (RNNs) those are separated into different condition. There is selector system that decides certain RNN system depending the current condition of the robot. In this paper, we implemented this system in pendulum mobile robot as the basic object of study. Several certain number of RNNs are implemented into certain different condition of tilt robot. RNN works alternately depending on the condition of robot. In order to prove the effectiveness of the proposed model, we simulated in the computer simulation Open Dynamic Engine (ODE) and compared with ordinary RNN. The proposed neural model successfully stabilize the applied robot (2-wheeled robot). This model is developed for implemented into humanoid balancing learning system as the final object of study.

AB - Biologically inspired control system is necessary to be increased. This paper proposed the new design of multimodal neural network inspired from human learning system which takes different action in different condition. The multimodal neural network consists of some recurrent neural networks (RNNs) those are separated into different condition. There is selector system that decides certain RNN system depending the current condition of the robot. In this paper, we implemented this system in pendulum mobile robot as the basic object of study. Several certain number of RNNs are implemented into certain different condition of tilt robot. RNN works alternately depending on the condition of robot. In order to prove the effectiveness of the proposed model, we simulated in the computer simulation Open Dynamic Engine (ODE) and compared with ordinary RNN. The proposed neural model successfully stabilize the applied robot (2-wheeled robot). This model is developed for implemented into humanoid balancing learning system as the final object of study.

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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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A2 - Kiguchi, Kazuo

PB - Springer Verlag

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ER -

Saputra AA, Sulistijono IA, Kubota N. Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot. In Liu H, Kubota N, Obo T, Kiguchi K, editors, Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings. Springer Verlag. 2016. p. 596-608. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-43506-0_52

Multimodal recurrent neural network (MRNN) based self balancing system: Applied into two-wheeled robot

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this