Titre :

Visual Haptic Feedback for Training of Robotic Suturing

Auteur(s) :

Jourdes, François [Auteur]
InSimo
Valentin, Brice [Auteur]
InSimo
Allard, Jérémie [Auteur]
InSimo
Duriez, Christian [Auteur]
Deformable Robots Simulation Team [DEFROST ]
Seeliger, Barbara [Auteur]
l'Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD)
Centre Hospitalier Universitaire [Strasbourg] [CHU Strasbourg]
L'Institut hospitalo-universitaire de Strasbourg [IHU Strasbourg]
Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie [ICube]

Titre de la revue :

Frontiers in Robotics and AI

Éditeur :

Frontiers Media S.A.

Date de publication :

2022

ISSN :

2296-9144

Mot(s)-clé(s) en anglais :

collision detection
haptics
knot-tying
minimally invasive surgery
robotic surgery training
surgical simulation
virtual reality

Discipline(s) HAL :

Informatique [cs]/Robotique [cs.RO]

Résumé en anglais : [en]

Current surgical robotic systems are teleoperated and do not have force feedback. Considerable practice is required to learn how to use visual input such as tissue deformation upon contact as a substitute for tactile sense. ...
Lire la suite >Current surgical robotic systems are teleoperated and do not have force feedback. Considerable practice is required to learn how to use visual input such as tissue deformation upon contact as a substitute for tactile sense. Thus, unnecessarily high forces are observed in novices, prior to specific robotic training, and visual force feedback studies demonstrated reduction of applied forces. Simulation exercises with realistic suturing tasks can provide training outside the operating room. This paper presents contributions to realistic interactive suture simulation for training of suturing and knot-tying tasks commonly used in robotically-assisted surgery. To improve the realism of the simulation, we developed a global coordinate wire model with a new constraint development for the elongation. We demonstrated that a continuous modeling of the contacts avoids instabilities during knot tightening. Visual cues are additionally provided, based on the computation of mechanical forces or constraints, to support learning how to dose the forces. The results are integrated into a powerful system-agnostic simulator, and the comparison with equivalent tasks performed with the da Vinci Xi system confirms its realism.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Institut de Chirurgie Mini-Invasive guidée par l'Image

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL