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Learning the Complete Shape of Concentric Tube Robots
Alan Kuntz1, Armaan Sethi2, Robert J Webster3
1Robotics Center and the School of Computing, University of Utah, Salt Lake City, UT, 84112 USA.
IEEE Transactions on Medical Robotics and Bionics
|May 27, 2020
Summary
A new deep learning model accurately estimates the shape of concentric tube robots for safer minimally invasive surgery. This advancement improves robotic surgery planning in complex anatomical spaces.
Area of Science:
- Robotics
- Medical Engineering
- Artificial Intelligence
Background:
- Concentric tube robots offer potential for minimally invasive surgery in challenging anatomical locations.
- Accurate robot shape estimation is crucial for safe surgical motion planning, especially in constrained environments.
- Existing physics-based models struggle with complex physical phenomena, limiting their surgical accuracy.
Purpose of the Study:
- To develop a novel, accurate model for estimating the complete shape of concentric tube robots.
- To overcome the limitations of current physics-based models in surgical applications.
Main Methods:
- A deep neural network model was developed for shape estimation.
- The model was trained using a combination of simulated and real-world data.
- Multiple deep neural network architectures were evaluated for performance.
Main Results:
- The learned model demonstrated high accuracy in computing the full shape of concentric tube robots.
- The proposed model offers a significant improvement over existing methods for shape estimation.
Conclusions:
- The developed deep learning model provides an accurate solution for concentric tube robot shape estimation.
- This advancement can enhance the safety and efficacy of robotic minimally invasive surgery.
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