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An improved path planning algorithm based on artificial potential field and primal-dual neural network for surgical
Linjia Hao1, Dongdong Liu1, Shuxian Du1
1School of Biomedical Engineering, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, China.
Computer Methods and Programs in Biomedicine
|November 10, 2022
Summary
This study introduces an advanced path planning algorithm for spine surgery robots, enhancing safety and accuracy. The improved system effectively navigates complex surgical environments, minimizing errors for precise robotic-assisted procedures.
Area of Science:
- Robotics
- Surgical Navigation
- Artificial Intelligence
Background:
- Path planning is critical for safety and accuracy in surgical navigation systems.
- Existing algorithms face challenges like local minima and target proximity issues near obstacles.
- Enhancing the autonomy of spine surgery robots is crucial for improving surgical outcomes.
Purpose of the Study:
- To propose an improved path planning algorithm for spine surgery robots.
- To enhance the safety, accuracy, and autonomous capabilities of robotic-assisted spinal surgery.
- To address limitations of traditional path planning methods in complex surgical scenarios.
Main Methods:
- Modified Artificial Potential Field algorithm with dynamic gravitational constant and piecewise repulsion function.
- Constrained endpoint positions of the end-effector for precise pose control.
- Improved Primal-Dual Neural Network incorporating multiple constraints (path, obstacle avoidance, joint limits) to minimize joint angular velocity norm.
- Real-time planned velocity scheme to mitigate position error accumulation.
Main Results:
- The algorithm successfully generated collision-free trajectories for pedicle screw implantation.
- The robot accurately reached target positions in complex simulated scenarios.
- Achieved high precision with endpoint errors below 0.1 mm and maximum position errors around 0.05 mm.
- Validated effectiveness through real-world experiments.
Conclusions:
- The proposed path planning algorithm significantly improves the safety and accuracy of spine surgery robots.
- The enhanced algorithm effectively overcomes common path planning challenges in autonomous surgical systems.
- The method demonstrates practical applicability and potential for advancing robotic-assisted spinal surgery.
