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Multilevel Fuzzy Control Based on Force Information in Robot-Assisted Decompressive Laminectomy
Xiaozhi Qi1, Yu Sun1, Xiaohang Ma1
1Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Advances in Experimental Medicine and Biology
|October 12, 2018
Summary
This study introduces a robot-assisted surgery technique for lumbar spinal stenosis (LSS) using a multilevel fuzzy controller to stabilize grinding forces. This improves surgical accuracy and robotic performance during decompressive laminectomy.
Area of Science:
- Orthopedic Surgery
- Robotics in Medicine
- Control Systems Engineering
Background:
- Lumbar spinal stenosis (LSS) is an orthopedic condition causing neurological symptoms.
- Decompressive laminectomy, involving vertebral lamina grinding, is a key treatment for LSS.
- Robot-assisted surgery with image-guided navigation enhances precision and reduces surgeon burden.
Purpose of the Study:
- To propose a multilevel fuzzy control system for robot-assisted decompressive laminectomy.
- To improve surgical quality and robotic dynamic performance during LSS treatment.
- To enhance the safety and accuracy of vertebral lamina grinding.
Main Methods:
- A multilevel fuzzy controller adjusts feed rate based on force feedback to maintain stable grinding forces.
- Controlled grinding paths are planned using 3D reconstructed medical images.
- Navigation registration maps the robot to the surgical images.
- Texture recognition of bone is used for component classification.
Main Results:
- The fuzzy control system effectively stabilizes grinding forces despite variations in vertebral bone properties.
- Accurate classification of bone components is achieved through texture analysis.
- The system demonstrates stable dynamic performance during simulated surgical operations.
Conclusions:
- The proposed multilevel fuzzy control enhances robot-assisted decompressive laminectomy for LSS.
- Stable grinding forces and accurate bone component recognition improve surgical safety and precision.
- This approach offers improved robotic dynamic performance for orthopedic spinal surgery.
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