Learning medical suturing primitives for autonomous suturing

Name: Learning medical suturing primitives for autonomous suturing
Author: Amirshirzad, Negin, Sunal, Begüm, Bebek, Özkan, Öztop, Erhan

İsim	Learning medical suturing primitives for autonomous suturing
Yazar	Amirshirzad, Negin, Sunal, Begüm, Bebek, Özkan, Öztop, Erhan
Basım Tarihi:	2021
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166541873-7
Kayıt Numarası	639d695e-2dbf-4eb2-ba5d-ee96677f8d0e
Lokasyon	Computer Science, Mechanical Engineering
Tarih	2021
Notlar	TÜBİTAK
Örnek Metin	This paper focuses on a learning from demonstration approach for autonomous medical suturing. A conditional neural network is used to learn and generate suturing primitives trajectories which were conditioned on desired context points. Using our designed GUI a user could plan and select suturing insertion points. Given the insertion point our model generates joint trajectories on real time satisfying this condition. The generated trajectories combined with a kinematic feedback loop were used to drive an 11-DOF robotic system and shows satisfying abilities to learn and perform suturing primitives autonomously having only a few demonstrations of the movements.
DOI	10.1109/CASE49439.2021.9551415
Cilt	2021

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Learning medical suturing primitives for autonomous suturing

Yazar Amirshirzad, Negin, Sunal, Begüm, Bebek, Özkan, Öztop, Erhan

Basım Tarihi 2021

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166541873-7

Kayıt Numarası 639d695e-2dbf-4eb2-ba5d-ee96677f8d0e

Lokasyon Computer Science, Mechanical Engineering

Tarih 2021

Notlar TÜBİTAK

Örnek Metin This paper focuses on a learning from demonstration approach for autonomous medical suturing. A conditional neural network is used to learn and generate suturing primitives trajectories which were conditioned on desired context points. Using our designed GUI a user could plan and select suturing insertion points. Given the insertion point our model generates joint trajectories on real time satisfying this condition. The generated trajectories combined with a kinematic feedback loop were used to drive an 11-DOF robotic system and shows satisfying abilities to learn and perform suturing primitives autonomously having only a few demonstrations of the movements.

DOI 10.1109/CASE49439.2021.9551415

Cilt 2021