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Updated: May 13, 2026

Bioinspired Soft Robot with Incorporated Microelectrodes

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

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Bioinspiration & biomimetics·2024

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.

Keywords:

Bioinspired sensing Fiber optic receptor Multitask cascade neural network Non-destructive tissue adhesion Robotic surgery assistance

Related Experiment Videos

Last Updated: May 13, 2026

Bioinspired Soft Robot with Incorporated Microelectrodes

Bioinspired Soft Robot with Incorporated Microelectrodes

Published on: February 28, 2020

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.