Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction
In robotic-assisted laminectomy decompression, stable and precise vertebral plate cutting remains challenging due to manual dependency and the absence of adaptive skill-learning mechanisms. This paper presents an advanced robotic vertebral plate-cutting system that leverages patient-specific anatomi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/12/719 |
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| author | Heqiang Tian Xiang Zhang Yurui Yin Hongqiang Ma |
| author_facet | Heqiang Tian Xiang Zhang Yurui Yin Hongqiang Ma |
| author_sort | Heqiang Tian |
| collection | DOAJ |
| description | In robotic-assisted laminectomy decompression, stable and precise vertebral plate cutting remains challenging due to manual dependency and the absence of adaptive skill-learning mechanisms. This paper presents an advanced robotic vertebral plate-cutting system that leverages patient-specific anatomical variations and replicates the surgeon’s cutting technique through a trajectory parameter prediction model. A spatial mapping relationship between artificial and patient vertebrae is first established, enabling the robot to mimic surgeon-defined trajectories with high accuracy. The robotic system’s trajectory planning begins with acquiring point cloud data of the vertebral plate, which undergoes preprocessing, Non-Uniform Rational B-Splines (NURBS) fitting, and parametric discretization. Using the processed data, a spatial mapping method translates the surgeon’s cutting path to the robotic coordinate system, with simulation validating the trajectory’s adherence to surgical requirements. To further enhance the accuracy and stability of trajectory planning, a Backpropagation(BP) neural network is implemented, providing predictive modeling for trajectory parameters. The analysis and training of the neural network confirm its effectiveness in capturing complex cutting trajectories. Finally, experimental validation, involving an artificial vertebral body model and cutting trials on patient vertebrae, demonstrates the proposed method’s capability to deliver enhanced cutting precision and stability. This skill-learning-based, personalized trajectory planning approach offers significant potential for improving the safety and quality of orthopedic robotic surgeries. |
| format | Article |
| id | doaj-art-5b3835367bda46bb9b2fcdf3143bd79a |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-5b3835367bda46bb9b2fcdf3143bd79a2025-08-20T02:50:56ZengMDPI AGBiomimetics2313-76732024-11-0191271910.3390/biomimetics9120719Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network PredictionHeqiang Tian0Xiang Zhang1Yurui Yin2Hongqiang Ma3College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaIn robotic-assisted laminectomy decompression, stable and precise vertebral plate cutting remains challenging due to manual dependency and the absence of adaptive skill-learning mechanisms. This paper presents an advanced robotic vertebral plate-cutting system that leverages patient-specific anatomical variations and replicates the surgeon’s cutting technique through a trajectory parameter prediction model. A spatial mapping relationship between artificial and patient vertebrae is first established, enabling the robot to mimic surgeon-defined trajectories with high accuracy. The robotic system’s trajectory planning begins with acquiring point cloud data of the vertebral plate, which undergoes preprocessing, Non-Uniform Rational B-Splines (NURBS) fitting, and parametric discretization. Using the processed data, a spatial mapping method translates the surgeon’s cutting path to the robotic coordinate system, with simulation validating the trajectory’s adherence to surgical requirements. To further enhance the accuracy and stability of trajectory planning, a Backpropagation(BP) neural network is implemented, providing predictive modeling for trajectory parameters. The analysis and training of the neural network confirm its effectiveness in capturing complex cutting trajectories. Finally, experimental validation, involving an artificial vertebral body model and cutting trials on patient vertebrae, demonstrates the proposed method’s capability to deliver enhanced cutting precision and stability. This skill-learning-based, personalized trajectory planning approach offers significant potential for improving the safety and quality of orthopedic robotic surgeries.https://www.mdpi.com/2313-7673/9/12/719robotic-assisted laminectomyvertebral plate cuttingtrajectory planningskill learningBP neural network |
| spellingShingle | Heqiang Tian Xiang Zhang Yurui Yin Hongqiang Ma Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction Biomimetics robotic-assisted laminectomy vertebral plate cutting trajectory planning skill learning BP neural network |
| title | Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction |
| title_full | Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction |
| title_fullStr | Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction |
| title_full_unstemmed | Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction |
| title_short | Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction |
| title_sort | skill learning based trajectory planning for robotic vertebral plate cutting personalization through surgeon technique integration and neural network prediction |
| topic | robotic-assisted laminectomy vertebral plate cutting trajectory planning skill learning BP neural network |
| url | https://www.mdpi.com/2313-7673/9/12/719 |
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