Design Optimization Algorithms for Concentric Tube Robots
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Baykal, Cenk. Design Optimization Algorithms for Concentric Tube Robots. 2015. https://doi.org/10.17615/c6d5-zw95APA
Baykal, C. (2015). Design Optimization Algorithms for Concentric Tube Robots. https://doi.org/10.17615/c6d5-zw95Chicago
Baykal, Cenk. 2015. Design Optimization Algorithms for Concentric Tube Robots. https://doi.org/10.17615/c6d5-zw95- Last Modified
- February 26, 2019
- Creator
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Baykal, Cenk
- Affiliation: College of Arts and Sciences, Department of Computer Science
- Abstract
- Concentric tube robots are tentacle-like surgical robots that can bend around anatomical obstacles to access hard-to-reach surgical targets. These robots have potential to enable minimally invasive surgical procedures by allowing physicians to access clinical regions that were previously unreachable using traditional instruments. Concentric tube robots are composed of nested, customizable tubes which undergo complicated mechanical interactions that generate tentacle-like motion. As a consequence of this intricate kinematic mechanism, the physical specifications of each of the robots tubes, i.e. the robot’s design, significantly affect the shapes that the robot can undertake and the regions it can reach. Customizing the design of these robots can potentially facilitate successful surgical procedures on a variety of patients. In this thesis, we present design optimization algorithms to generate appropriate design parameters on an application- and patient-specific basis. We consider three design optimization problems. First, we present a design optimization algorithm that generates a concentric tube robot design under which the robot can maximize the reachable volume of a given goal region in the human body. We provide analysis establishing that our design optimization algorithm for generating a single design is asymptotically optimal. Second, we present an algorithm that computes sets of concentric tube robot designs that can collectively maximize the reachable volume of a given goal region in the human body. Third, we introduce an algorithm that generates the set of designs of minimal size such that the designs in the set can collectively reach a physician-specified percentage of the goal region. Each of our algorithms combines a search in the design space of a concentric tube robot using Adaptive Simulated Annealing with a sampling-based motion planner in the robot’s configuration space in order to find a single or sets of designs that enable paths to the goal regions while avoiding contact with anatomical obstacles. We demonstrate the effectiveness of each of our algorithms in a simulated scenario based on lung anatomy and compare our algorithms’ performance with that of current state-of-the-art design optimization algorithms.
- Date of publication
- spring 2015
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- In Copyright
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- Funding: Summer Undergraduate Research Fellowship, NIH
- Funding: Dunlevie Honors Undergraduate Research Fund
- Advisor
- Alterovitz, Ron
- Degree
- Bachelor of Science
- Honors level
- Highest Honors
- Degree granting institution
- University of North Carolina at Chapel Hill
- Extent
- 41
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