Self collision detection system for R3 robot

Yakup Özden; Hatice Köse

doi:10.1109/EHB.2015.7391440

Self collision detection system for R3 robot

Yakup Özden
, Hatice Köse

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

Özet

This work is part of a project for sign language tutoring with imitation based interactive game, iSign1. An assistive social humanoid robot (R3) is accompanying deaf children in the interaction game. The robot interacts with children using visual modules, including sign recognition and sign generation. This paper focuses on upper torso self collision detection system for the humanoid robot R3, which is used in sign generation step in the game. Three approaches including a neuro-fuzzy, a multi neuro-fuzzy and a multi neural-network approach based on the arm joint positions and orientations are implemented and the results are presented.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2015 E-Health and Bioengineering Conference, EHB 2015
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781467375450
DOI'lar	https://doi.org/10.1109/EHB.2015.7391440
Yayın durumu	Yayınlandı - 25 Oca 2016
Etkinlik	5th IEEE International Conference on E-Health and Bioengineering, EHB 2015 - Iasi, Romania Süre: 19 Kas 2015 → 21 Kas 2015

Yayın serisi

Adı	2015 E-Health and Bioengineering Conference, EHB 2015

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	5th IEEE International Conference on E-Health and Bioengineering, EHB 2015
Ülke/Bölge	Romania
Şehir	Iasi
Periyot	19/11/15 → 21/11/15

Bibliyografik not

Publisher Copyright:
© 2015 IEEE.

Finansman

This work was supported by The Scientific and Technological Research Council of Turkey under the contract TUBITAK KARIYER 111E283

Finansörler	Finansör numarası
TUBITAK KARIYER	111E283
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Belgeye Erişim

10.1109/EHB.2015.7391440

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@inproceedings{09fa73092453404989466e07321dbc04,

title = "Self collision detection system for R3 robot",

abstract = "This work is part of a project for sign language tutoring with imitation based interactive game, iSign1. An assistive social humanoid robot (R3) is accompanying deaf children in the interaction game. The robot interacts with children using visual modules, including sign recognition and sign generation. This paper focuses on upper torso self collision detection system for the humanoid robot R3, which is used in sign generation step in the game. Three approaches including a neuro-fuzzy, a multi neuro-fuzzy and a multi neural-network approach based on the arm joint positions and orientations are implemented and the results are presented.",

keywords = "Human-robot interaction, neuro-fuzzy systems, self collision detection, sign language",

author = "Yakup {\"O}zden and Hatice K{\"o}se",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 5th IEEE International Conference on E-Health and Bioengineering, EHB 2015 ; Conference date: 19-11-2015 Through 21-11-2015",

year = "2016",

month = jan,

day = "25",

doi = "10.1109/EHB.2015.7391440",

language = "English",

series = "2015 E-Health and Bioengineering Conference, EHB 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 E-Health and Bioengineering Conference, EHB 2015",

address = "United States",

}

Özden, Y & Köse, H 2016, Self collision detection system for R3 robot. in 2015 E-Health and Bioengineering Conference, EHB 2015., 7391440, 2015 E-Health and Bioengineering Conference, EHB 2015, Institute of Electrical and Electronics Engineers Inc., 5th IEEE International Conference on E-Health and Bioengineering, EHB 2015, Iasi, Romania, 19/11/15. https://doi.org/10.1109/EHB.2015.7391440

TY - GEN

T1 - Self collision detection system for R3 robot

AU - Özden, Yakup

AU - Köse, Hatice

PY - 2016/1/25

Y1 - 2016/1/25

N2 - This work is part of a project for sign language tutoring with imitation based interactive game, iSign1. An assistive social humanoid robot (R3) is accompanying deaf children in the interaction game. The robot interacts with children using visual modules, including sign recognition and sign generation. This paper focuses on upper torso self collision detection system for the humanoid robot R3, which is used in sign generation step in the game. Three approaches including a neuro-fuzzy, a multi neuro-fuzzy and a multi neural-network approach based on the arm joint positions and orientations are implemented and the results are presented.

AB - This work is part of a project for sign language tutoring with imitation based interactive game, iSign1. An assistive social humanoid robot (R3) is accompanying deaf children in the interaction game. The robot interacts with children using visual modules, including sign recognition and sign generation. This paper focuses on upper torso self collision detection system for the humanoid robot R3, which is used in sign generation step in the game. Three approaches including a neuro-fuzzy, a multi neuro-fuzzy and a multi neural-network approach based on the arm joint positions and orientations are implemented and the results are presented.

KW - Human-robot interaction

KW - neuro-fuzzy systems

KW - self collision detection

KW - sign language

UR - https://www.scopus.com/pages/publications/84978063162

U2 - 10.1109/EHB.2015.7391440

DO - 10.1109/EHB.2015.7391440

M3 - Conference contribution

AN - SCOPUS:84978063162

T3 - 2015 E-Health and Bioengineering Conference, EHB 2015

BT - 2015 E-Health and Bioengineering Conference, EHB 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on E-Health and Bioengineering, EHB 2015

Y2 - 19 November 2015 through 21 November 2015

ER -

Self collision detection system for R3 robot

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap