Stereo Visual Servoing Control of a Soft Endoscope for Upper Gastrointestinal Endoscopic Submucosal Dissection.

endoscopic submucosal dissection soft robot stereo visual servoing control surgical robot

Journal

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

Informations de publication

Date de publication:
15 Feb 2024
Historique:
received: 04 01 2024
revised: 07 02 2024
accepted: 14 02 2024
medline: 24 2 2024
pubmed: 24 2 2024
entrez: 24 2 2024
Statut: epublish

Résumé

Quickly and accurately completing endoscopic submucosal dissection (ESD) operations within narrow lumens is currently challenging because of the environment's high flexibility, invisible collision, and natural tissue motion. This paper proposes a novel stereo visual servoing control for a dual-segment robotic endoscope (DSRE) for ESD surgery. Departing from conventional monocular-based methods, our DSRE leverages stereoscopic imaging to rapidly extract precise depth data, enabling quicker controller convergence and enhanced surgical accuracy. The system's dual-segment configuration enables agile maneuverability around lesions, while its compliant structure ensures adaptability within the surgical environment. The implemented stereo visual servo controller uses image features for real-time feedback and dynamically updates gain coefficients, facilitating rapid convergence to the target. In visual servoing experiments, the controller demonstrated strong performance across various tasks. Even when subjected to unknown external forces, the controller maintained robust performance in target tracking. The feasibility and effectiveness of the DSRE were further verified through ex vivo experiments. We posit that this novel system holds significant potential for clinical application in ESD surgeries.

Identifiants

pubmed: 38399005
pii: mi15020276
doi: 10.3390/mi15020276
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Sichuan Science and Technology Program
ID : 2023YFH0093

Auteurs

Jian Chen (J)

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Centre of AI and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.

Shuai Wang (S)

Centre of AI and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.

Qingxiang Zhao (Q)

Centre of AI and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.

Wei Huang (W)

Centre of AI and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.

Mingcong Chen (M)

Department of Biomedical Engineering, City University of Hong Kong, Hong Kong.

Jian Hu (J)

Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Centre of AI and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.

Yihe Wang (Y)

Centre of AI and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.

Hongbin Liu (H)

Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Centre of AI and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong.
School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EU, UK.

Classifications MeSH