Design and Experimental Validation of a 3D-Printed Embedded-Sensing Continuum Robot for Neurosurgery.

hyper-redundant manipulator medical robotics multiple regression model optoelectronic sensor

Journal

Micromachines
ISSN: 2072-666X
Titre abrégé: Micromachines (Basel)
Pays: Switzerland
ID NLM: 101640903

Informations de publication

Date de publication:
06 Sep 2023
Historique:
received: 31 07 2023
revised: 28 08 2023
accepted: 04 09 2023
medline: 28 9 2023
pubmed: 28 9 2023
entrez: 28 9 2023
Statut: epublish

Résumé

A minimally-invasive manipulator characterized by hyper-redundant kinematics and embedded sensing modules is presented in this work. The bending angles (tilt and pan) of the robot tip are controlled through tendon-driven actuation; the transmission of the actuation forces to the tip is based on a Bowden-cable solution integrating some channels for optical fibers. The viability of the real-time measurement of the feedback control variables, through optoelectronic acquisition, is evaluated for automated bending of the flexible endoscope and trajectory tracking of the tip angles. Indeed, unlike conventional catheters and cannulae adopted in neurosurgery, the proposed robot can extend the actuation and control of snake-like kinematic chains with embedded sensing solutions, enabling real-time measurement, robust and accurate control of curvature, and tip bending of continuum robots for the manipulation of cannulae and microsurgical instruments in neurosurgical procedures. A prototype of the manipulator with a length of 43 mm and a diameter of 5.5 mm has been realized via 3D printing. Moreover, a multiple regression model has been estimated through a novel experimental setup to predict the tip angles from measured outputs of the optoelectronic modules. The sensing and control performance has also been evaluated during tasks involving tip rotations.

Identifiants

pubmed: 37763906
pii: mi14091743
doi: 10.3390/mi14091743
pmc: PMC10535800
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Donatella Dragone (D)

Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy.

Francesca Federica Donadio (FF)

Biomechatronics Laboratory, Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario "S. Venuta", 88100 Catanzaro, Italy.

Chiara Mirabelli (C)

Biomechatronics Laboratory, Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario "S. Venuta", 88100 Catanzaro, Italy.

Carlo Cosentino (C)

Biomechatronics Laboratory, Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario "S. Venuta", 88100 Catanzaro, Italy.

Francesco Amato (F)

Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy.

Paolo Zaffino (P)

Biomechatronics Laboratory, Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario "S. Venuta", 88100 Catanzaro, Italy.

Maria Francesca Spadea (MF)

Biomechatronics Laboratory, Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario "S. Venuta", 88100 Catanzaro, Italy.

Domenico La Torre (D)

Department of Medical and Surgical Sciences, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario "S. Venuta", 88100 Catanzaro, Italy.

Alessio Merola (A)

Biomechatronics Laboratory, Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario "S. Venuta", 88100 Catanzaro, Italy.

Classifications MeSH