A Full-Range Proximity-Tactile Sensor Based on Multimodal Perception Fusion for Minimally Invasive Surgical Robots.

Area of Science:

• Robotics
• Biomedical Engineering
• Sensor Technology

Background:

• Minimally invasive surgical robots offer benefits but lack sufficient perception.
• Adequate sensing is crucial for safe and effective robotic surgery.

Purpose of the Study:

• To develop a full-range proximity-tactile sensing module for surgical robots.
• To enhance the safety and diagnostic capabilities of robot-assisted surgery.

Main Methods:

• Developed a multimodal sensing module integrating ultrasonic (MEMS pMUTs), capacitive, and triboelectric sensors.
• Implemented a wireless vibration feedback wristband and digital-twin interface.
• Utilized convolutional neural networks for tissue identification based on ultrasound echoes.

Main Results:

• The system demonstrated enhanced safety for surgical robots.
• Achieved 91.6% accuracy in preliminary detection of subcutaneous abnormal tissues.
• The integrated sensors provide accurate proximity-tactile perception.

Conclusions:

• The developed sensor module significantly improves surgical robot safety and perception.
• It offers potential for initial diagnostics and advances intelligent robotic surgery.