Flexible FBG Sensor for Real-Time Shape Reconstruction
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Solution Overview
Problem
Existing surface shape sensors are limited by their rigidity, inability to stretch, and require precise placement of sensing elements, making them unsuitable for flexible and deformable robotic applications, particularly in soft robotics where real-time 3D surface shape reconstruction is needed.
Innovation Solution
A flexible surface shape sensor design featuring sparsely distributed Fiber Bragg Gratings (FBGs) embedded in a silicone rubber substrate, utilizing finite-element analysis (FEA) for customization and machine learning algorithms for real-time 3D surface reconstruction, allowing for adaptable and scalable sensing without the need for extensive connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If grid-type MEMS sensors are used for surface shape reconstruction, then local position and orientation information can be obtained, but the sensor has limited flexibility and ability to stretch due to fixed connections between nodes
Solution Approach 1:
The patent replaces rigid grid-type MEMS sensors with a flexible substrate containing sparsely distributed strain-sensitive sensors. This flexible film approach eliminates the fixed connections between nodes that limited previous sensors, allowing the sensor to conform to and stretch with soft robotic surfaces while maintaining shape reconstruction capability through sparse sensing points.
Solution Approach 2:
Instead of using a continuous rigid grid structure, the patent segments the sensing function into sparsely distributed independent strain-sensitive sensors on a flexible substrate. This segmentation allows each sensor to independently measure local strain while the flexible substrate accommodates overall deformation, resolving the contradiction between measurement precision and flexibility.
2Adaptability or versatility
If liquid conductor sensors are used in elastomeric substrates, then inherent compliance is achieved, but contact electrodes on either end of liquid-metal channels are required making scaling challenging
Solution Approach 1:
The patent extracts the electrode connection requirement from the sensing mechanism by using strain-sensitive sensors that do not require contact electrodes on either end of channels. This eliminates the scaling challenge associated with liquid conductor sensors while maintaining inherent compliance through the flexible substrate integration.
Solution Approach 2:
The patent creates a universal sensor platform that combines flexible substrate integration with simplified strain-sensitive sensing elements. This universal design can be scaled to different sizes and configurations without requiring complex electrode networks, enabling broad applicability across soft robotic systems.
3Productivity
If FBGs are placed in curvilinear layout on wearable sensing glove, then hand motion can be monitored in real time, but accurate placement of FBGs is required to align with finger joints
Solution Approach 1:
The patent uses sparsely distributed sensors instead of requiring complete coverage with precisely placed sensors at every joint location. This partial sensing approach, combined with shape reconstruction algorithms, achieves real-time monitoring without the manufacturing precision requirements of aligning sensors with every finger joint.
Solution Approach 2:
The patent replaces the mechanical alignment requirement (physically positioning FBGs at joint locations) with a computational approach using sparse strain measurements and shape reconstruction algorithms. This substitution eliminates the need for precise mechanical placement while maintaining real-time monitoring capability.
4Measurement precision
If orthogonal FBG fiber layout is used for surface shape sensing, then convex and concave object surfaces can be detected, but the ability to stretch is limited and precise FBG positioning is required
Solution Approach 1:
The patent replaces the rigid orthogonal fiber layout with a flexible substrate that can stretch and deform. This flexible film approach maintains the ability to detect convex and concave surfaces through sparse strain-sensitive sensors while eliminating the stretchability limitations and precise positioning requirements of orthogonal fiber networks.
Solution Approach 2:
The patent changes the fundamental parameter of sensor substrate from rigid (orthogonal fiber layout) to flexible (stretchable substrate with sparse sensors). This parameter change enables stretchability while maintaining surface shape detection capability through computational reconstruction from sparse measurements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time reconstruction of complex surface shapes, including bending and twisting, with improved flexibility and accuracy, reducing fabrication errors and enabling integration into soft robotic systems without the limitations of traditional sensors.
Implementation Method 1
Along the length of the optical fiber strain-sensitive sensing elements in the form of Fiber Bragg Gratings (FBGs) are placed. These FBGs can detect localized deformations of the fiber.
Implementation Method 2
The sensor is capable of measuring shape changes by transferring the sensor strains to the optical fiber during sensor deformation.
Data Source
AI summary
A surface shape determination system includes a surface shape sensor in the form of a flexible and stretchable elastomeric substrate with strain/displacement sensing elements embedded in it. The sensor may be a single-core optical fiber with a series of fiber Bragg Gratings (FBGs) located at predetermined positions along its length. A light source provides an incident light spectrum at one end of the fiber. Each grating of the fiber has index modulation which causes particular wavelengths of the light spectrum that do not satisfy the Bragg condition to be reflected back in the fiber. The refractive index of each grating changes with strain on the substrate due to deflection of it. An interrogator captures the reflected wavelengths and retrieves signal information therefrom. A processor receives the output of the interrogator and performs non-linear regression analysis on the information using a neural network to reconstruct the surface morphology in real-time.