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Semi-autonomous Simulated Brain Tumor Ablation with RavenII Surgical Robot using Behavior Tree
Danying Hu1, Yuanzheng Gong2, Blake Hannaford1
1Biorobotics Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
IEEE International Conference on Robotics and Automation : ICRA : [Proceedings]. IEEE International Conference on Robotics and Automation
|September 26, 2015
Summary
This study introduces semi-autonomous robotic surgery for brain tumor ablation using the RAVEN robot and behavior trees. This approach enhances surgical precision and efficiency by automating repetitive tasks.
Area of Science:
- Neurosurgery
- Robotics
- Artificial Intelligence
Background:
- Medical robots assist surgeons in complex procedures, but most require direct or indirect human control.
- Introducing autonomy in robotic surgery can alleviate surgeons from repetitive tasks and leverage robotic precision.
- Brain tumor ablation is a complex procedure that can benefit from enhanced robotic assistance.
Purpose of the Study:
- To present a semi-autonomous neurosurgical procedure for brain tumor ablation.
- To model and implement the surgical task using a behavior tree framework.
- To design and conduct a simulated ablation task for feasibility and performance analysis.
Main Methods:
- Utilized the RAVEN Surgical Robot for the procedure.
- Integrated a behavior tree framework to model the semi-autonomous surgical task.
- Employed stereo visual feedback for enhanced guidance and control.
- Designed a simulated brain tumor ablation task for experimental validation.
Main Results:
- Successfully modeled and implemented a semi-autonomous brain tumor ablation procedure.
- Demonstrated the feasibility of using behavior trees for flexible and intelligent task management in robotic surgery.
- Analyzed robot performance in a simulated environment, highlighting accuracy and dexterity benefits.
Conclusions:
- Brain tumor ablation is a suitable candidate for semi-autonomous robotic surgery.
- The behavior tree framework provides an effective method for modeling complex semi-autonomous surgical tasks.
- The developed system shows promise for improving precision and efficiency in neurosurgical interventions.
