Development of a colon endoscope robot that adjusts its locomotion through the use of reinforcement learning

G. Trovato, M. Shikanai, G. Ukawa, J. Kinoshita, N. Murai, J. W. Lee, H. Ishii, A. Takanishi, K. Tanoue, S. Ieiri, K. Konishi, M. Hashizume

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11 Citations (Scopus)

Abstract

Purpose Fibre optic colonoscopy is usually performed with manual introduction and advancement of the endoscope, but there is potential for a robot capable of locomoting autonomously from the rectum to the caecum. A prototype robot was designed and tested. Methods The robot colonic endoscope consists in a front body with clockwise helical fin and a rear body with anticlockwise one, both connected via a DC motor. Input voltage is adjusted automatically by the robot, through the use of reinforcement learning, determining speed and direction (forward or backward). Results Experiments were performed both in-vitro and in-vivo, showing the feasibility of the robot. The device is capable of moving in a slippery environment, and reinforcement learning algorithms such as Q-learning and SARSA can obtain better results than simply applying full tension to the robot. Conclusions This self-propelled robotic endoscope has potential as an alternative to current fibre optic colonoscopy examination methods, especially with the addition of new sensors under development.

Original languageEnglish
Pages (from-to)317-325
Number of pages9
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume5
Issue number4
DOIs
Publication statusPublished - Jul 2010

All Science Journal Classification (ASJC) codes

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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    Trovato, G., Shikanai, M., Ukawa, G., Kinoshita, J., Murai, N., Lee, J. W., Ishii, H., Takanishi, A., Tanoue, K., Ieiri, S., Konishi, K., & Hashizume, M. (2010). Development of a colon endoscope robot that adjusts its locomotion through the use of reinforcement learning. International Journal of Computer Assisted Radiology and Surgery, 5(4), 317-325. https://doi.org/10.1007/s11548-010-0481-0