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Estimation of Tool-Tissue Forces in Robot-Assisted Minimally Invasive Surgery Using Neural Networks.

Sajeeva Abeywardena¹, Qiaodi Yuan¹, Antonia Tzemanaki¹

¹Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom.

Frontiers in Robotics and AI

|January 27, 2021

Summary

This summary is machine-generated.

This study introduces a novel algorithm for estimating tool-tissue force in robotic surgery without external sensors. Using motor current and neural networks, it enables real-time force estimation for improved haptic feedback.

Keywords:

force estimation haptic feedback minimally invasive surgery neural networks sensor-less sensing

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Published on: February 17, 2018

Area of Science:

Robotics
Surgical Technology
Artificial Intelligence

Background:

Minimally invasive surgery (MIS) often lacks direct tactile feedback.
Current methods for force sensing in robotic surgery can be complex or require external hardware.

Purpose of the Study:

To develop and validate a novel algorithm for estimating tool-tissue interaction forces in robot-assisted MIS.
To enable the provision of haptic feedback in robotic surgery without relying on external force sensors.

Main Methods:

The proposed algorithm utilizes the motor current data from surgical instruments.
Neural network models are employed to process motor current data and estimate force interactions.
Both offline and online testing methodologies were used to assess algorithm feasibility.

Main Results:

The developed algorithm successfully estimates tool-tissue force interaction.
The method demonstrates feasibility for online, real-time force estimation.
The findings suggest potential for enhanced haptic feedback in robotic surgical systems.

Conclusions:

The proposed algorithm offers a promising, sensor-less approach to estimating tool-tissue forces in robotic surgery.
This method could significantly advance the implementation of haptic feedback in robotic-assisted procedures.
Further development may lead to safer and more intuitive robotic surgical interventions.