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Human-Machine Interaction Methods for Minimally Invasive Surgical Robotic Arms.

Fenglin Jiang1, Ruiyao Jia2, Xiujuan Jiang1

  • 1Chengdu Second People's Hospital, Chengdu 610000, China.

Computational Intelligence and Neuroscience
|September 20, 2022
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Summary

Interactive minimally invasive surgical robot systems enhance safety and accuracy. This review explores human-robot interaction technologies, introducing fuzzy theory and reinforcement learning for improved surgical robot control.

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Area of Science:

  • Robotics
  • Surgical Technology
  • Human-Computer Interaction

Background:

  • Minimally invasive surgery (MIS) offers benefits like smaller incisions but has limitations in flexibility and control.
  • Interactive surgical robot systems aim to overcome MIS drawbacks by enhancing stability, safety, and accuracy.
  • Force feedback integration represents a significant advancement in controlling surgical robots during MIS.

Purpose of the Study:

  • To review the development status of interactive minimally invasive surgical robot systems.
  • To explore key technologies enabling effective human-robot interaction in surgical robotics.
  • To propose a novel human-robot interaction method for surgical robot posture adjustment.

Main Methods:

  • Review of existing literature on interactive minimally invasive surgical robotic systems.
  • Analysis of key technologies for human-robot interaction in surgical robotics.
  • Introduction of fuzzy theory and reinforcement learning into variable guide control models for parameter adjustment.

Main Results:

  • Identification of current trends and challenges in interactive surgical robot development.
  • Highlighting the importance of human-robot interaction for advanced surgical procedures.
  • Proposal of a new method for adjusting surgical robot posture using fuzzy theory and reinforcement learning.

Conclusions:

  • Interactive minimally invasive surgical robot systems are a promising direction for advancing surgical procedures.
  • Effective human-robot interaction is crucial for maximizing the potential of surgical robots.
  • The proposed method integrating fuzzy theory and reinforcement learning offers a novel approach to enhance surgical robot control and performance.