Human-Inspired Haptic Perception and Control in Robot-Assisted Milling Surgery.

Area of Science:

• Robotics
• Biomedical Engineering
• Surgical Technology

Background:

• Bone milling is a high-risk surgical procedure often reliant on surgeon experience.
• Enhancing safety in robot-assisted bone milling is crucial for patient outcomes.
• Human haptic perception offers a model for improving robotic surgical control.

Purpose of the Study:

• To enhance the safety of robot-assisted bone milling operations.
• To integrate human haptic perception principles into robotic surgical systems.
• To develop a system for real-time monitoring and control during bone milling.

Main Methods:

• A single-axis accelerometer was mounted on a surgical power tool to measure vibration.
• Acceleration signals were encoded into binary data and processed using a Hopfield network for state identification.
• A fuzzy logic controller was implemented to guide the robot's motion based on perceived milling states.

Main Results:

• The proposed system accurately discriminated between different milling states in porcine spine experiments.
• Effective safe motion control of the robot was achieved, even with significant noise.
• The system demonstrated robustness in identifying milling states under noisy conditions.

Conclusions:

• The integration of human haptic perception principles significantly improves robot-assisted bone milling safety.
• The developed system provides a reliable method for real-time state identification and robotic motion control.
• This approach offers a promising direction for safer and more precise robotic surgery.