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Optimizing Design Parameters for Sets of Concentric Tube Robots using Sampling-based Motion Planning.

Cenk Baykal¹, Luis G Torres¹, Ron Alterovitz¹

¹Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems

|March 9, 2016

Summary

This study introduces an algorithm to optimize concentric tube robot designs for medical procedures. The method enhances robot reachability in complex anatomy, improving access to clinical targets.

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Area of Science:

Robotics
Medical Robotics
Surgical Robotics

Background:

Concentric tube robots (CTRs) are flexible, tentacle-like medical robots designed for minimally invasive procedures.
CTRs can navigate complex anatomical pathways by bending around obstacles to reach clinical targets.
Customizing CTR design parameters, such as tube selection, is crucial for optimizing performance and workspace for specific medical tasks.

Purpose of the Study:

To develop an algorithm for generating optimal concentric tube robot designs tailored for specific anatomical regions and procedures.
To maximize the reachable workspace percentage within a target anatomical region using optimized CTR designs.
To ensure safe navigation by avoiding contact with anatomical obstacles during robotic movements.

Main Methods:

A global optimization method was employed to search the design space of CTRs, exploring various tube parameter combinations.
A sampling-based motion planner was integrated to assess robot configuration space and ensure obstacle-free paths.
The algorithm generates sets of CTR designs that collectively achieve maximum reachability to predefined goal regions.

Main Results:

The algorithm successfully identified sets of CTR designs that significantly increase the percentage of the reachable goal region.
Demonstrated the ability of optimized CTR designs to navigate complex anatomical structures, such as those found in the lungs.
Validated the effectiveness of the design optimization approach in a simulated lung anatomy scenario.

Conclusions:

The presented algorithm effectively optimizes concentric tube robot designs for enhanced reachability and maneuverability in complex anatomical environments.
This approach enables patient- and procedure-specific customization of medical robots, leading to improved clinical effectiveness.
The findings support the potential of optimized CTRs for safer and more efficient access to challenging-to-reach clinical targets.