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Bioinspired Soft Robot with Incorporated Microelectrodes
Published on: February 28, 2020
Bio-inspired fiber-optic-neural network enabled multi-physical sensing for tissue-safe robotic adhesion
Yuhan Zhang1, Ziwei Wang1, Huafu Zhang1
1Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China.
Biosensors & Bioelectronics
|May 11, 2026
Summary
This study introduces smart suction cups for robot-assisted surgery. These perceptive devices use multi-physical sensing to prevent tissue damage by monitoring adhesion forces and vacuum levels.
Area of Science:
- Biomedical Engineering
- Robotics
- Surgical Technology
Background:
- Current surgical suction cups lack perception, leading to over-adhesion and tissue damage.
- Robot-assisted surgery demands advanced tools for safe and non-destructive tissue manipulation.
Purpose of the Study:
- To develop a bionic suction cup with integrated sensing for real-time tissue damage monitoring.
- To enhance safety and precision in robot-assisted surgical procedures.
Main Methods:
- Integration of fiber optic receptors with a cascade neural network for multi-physical sensing.
- Real-time monitoring of mechanical compression, contact force, adhesion force, and vacuum level.
- Utilizing a physical model-guided neural network for data interpretation.
Main Results:
- Demonstrated real-time detection of critical mechanical parameters during suction adhesion.
- Enabled dynamic regulation of adhesion state based on sensory feedback.
- Successfully prevented tissue damage through intelligent adhesion control.
Conclusions:
- The proposed multi-physical sensing method provides a robust perceptual framework for surgical instruments.
- This technology paves the way for next-generation adhesion-based surgical robotics.
- Enhances non-destructive tissue manipulation in minimally invasive surgery.