Fiber Bragg grating-based touch-slip sensor for surface roughness detection.

Area of Science:

• Robotics
• Biomedical Engineering
• Materials Science

Background:

• Humanoid prosthetic hands require advanced sensory feedback for dexterous object manipulation.
• Existing sensors often lack the sensitivity or multi-modal capabilities needed for complex grasping tasks.

Purpose of the Study:

• To develop and validate a novel touch-slip sensor for humanoid prosthetic hands.
• To integrate three-dimensional force, sliding, and surface roughness detection into a single sensor unit.
• To improve the object identification and grasping accuracy of prosthetic devices.

Main Methods:

• Design of a double-layer sensing structure utilizing fiber Bragg grating (FBG) technology.
• Development of a contact sliding model and validation via finite element simulation.
• Experimental characterization of the sensor's performance in detecting 3D force, temperature, sliding, and surface roughness.
• Implementation of machine learning algorithms for data analysis and prediction.

Main Results:

• The FBG sensor successfully detected three-dimensional force, sliding information, and surface roughness.
• Temperature compensation was achieved using a reference grating.
• A regression prediction model demonstrated high accuracy in surface roughness detection (max error of 8.58×10⁻⁴).
• The sensor exhibits a simple structure, low cost, high linearity, and rapid response time.

Conclusions:

• The proposed FBG-based touch-slip sensor offers a viable solution for enhancing the sensory feedback of humanoid prosthetic hands.
• The sensor's multi-modal sensing capabilities and performance metrics pave the way for more intuitive and effective prosthetic control.
• This technology represents a significant advancement in the field of robotic prosthetics and human-machine interaction.