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Continuum Reconfigurable Parallel Robots for Surgery: Shape Sensing and State Estimation with Uncertainty.

Patrick L Anderson1, Arthur W Mahoney1, Robert J Webster1

  • 1Department of Mechanical Engineering at Vanderbilt University, Nashville, TN 37235, USA.

IEEE Robotics and Automation Letters
|November 22, 2017
PubMed
Summary

This study introduces shape sensing for novel CRISP surgical robots. A new framework combines sensor data and mechanics models to accurately estimate robot shape under load, enabling advanced surgical applications.

Keywords:
Flexible RobotsSurgical Robotics: LaparoscopySurgical Robotics: Steerable Catheters/Needles

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Area of Science:

  • Robotics
  • Medical Devices
  • Surgical Technology

Background:

  • Novel surgical robots, known as CRISP (Continuous Reconfiguration in vivo Surgical Platform) robots, utilize parallel flexible structures for minimally invasive surgery.
  • These robots can be reconfigured into complex structures for dexterous movement and force application within the body.
  • The elastic nature of CRISP robots leads to deformation under external forces, necessitating accurate shape estimation.

Purpose of the Study:

  • To develop and present a shape sensing framework for CRISP robots.
  • To enable precise estimation of robot shape under applied loads.
  • To facilitate future research in areas such as control, autonomous motion, and force sensing for these surgical robots.

Main Methods:

  • Integration of sensor information with mechanics-based models specific to CRISP robots.
  • Development of algorithms to estimate the robot's shape when subjected to external forces and perturbations.
  • Validation of the shape sensing framework through simulation and/or experimental data (details not provided in abstract).

Main Results:

  • A functional shape sensing framework for CRISP robots has been established.
  • The framework allows for the estimation of robot shapes under various applied loads.
  • The developed method addresses the challenge of deformation in elastic robotic structures.

Conclusions:

  • The shape sensing framework is crucial for advancing the capabilities of CRISP surgical robots.
  • This research lays the groundwork for enhanced control, autonomous operation, and force sensing in future surgical robot designs.
  • Accurate shape sensing is essential for the safe and effective deployment of reconfigurable, flexible surgical robots.