3.9 Article

Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot

期刊

FIBERS
卷 10, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/fib10120106

关键词

vascular interventional surgical robot; force feedback; endovascular catheterization; learning-based models

资金

  1. National Key Research and Development program of China
  2. National Natural Science Foundation of China
  3. Shenzhen Natural Science Foundation
  4. CAS PIFI
  5. [2019YFB1311700]
  6. [U21A20480]
  7. [61950410618]
  8. [JCYJ20190812173205538]

向作者/读者索取更多资源

Interventional therapy is an effective method for vascular-related diseases, but the lack of precise position control and force feedback remains a challenge. This study presents a master-slave VISR system that utilizes a fuzzy-PID controller and a neural network model to enhance navigation precision, response speed, and force measurement capabilities.
Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient's vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons' exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master-slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master-slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities.

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