Optical Force Inference via Deformable Wall and Neural Network
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Solution Overview
Problem
Existing sensor arrangements for robotic applications lack sufficient resolution and fragility for accurate force sensing, which limits their capability to move and manipulate objects effectively.
Innovation Solution
A method for force inference using a sensor arrangement with an elastically deformable wall, light sources, and an image sensor, where a feed-forward neural network is trained to calculate force maps from image data, enabling precise detection of forces, indenters, and their positions, directions, and shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If known sensor arrangements are used for force sensing in robotic applications, then the device structure is simple, but the measurement precision is insufficient and the reliability is poor due to fragility
Solution Approach 1:
The patent replaces traditional mechanical force sensors with an optical measurement system. A camera captures images of the measurement surface, and a neural network processes these images to infer force information. This substitution eliminates mechanical contact and fragility while achieving high measurement precision through optical detection and computational analysis.
Solution Approach 2:
The patent changes the measurement parameter from direct mechanical force detection to optical image analysis. By capturing surface deformation images and processing them through a trained neural network, the system transforms physical force information into visual data, enabling non-contact, high-precision force sensing with improved reliability.
2Measurement precision
If traditional force sensors are used, then the device complexity is low, but the measurement precision and multi-indenter detection capability are insufficient
Solution Approach 1:
The patent replaces complex mechanical sensor arrays with a simpler optical system consisting of a camera and processing unit. The measurement surface with reflective properties interacts with light sources, and the camera captures the resulting optical patterns. A neural network then processes these patterns to extract precise force information, achieving high measurement precision with reduced device complexity.
Solution Approach 2:
The patent introduces an optical intermediary system between the force application and detection. The measurement surface acts as an intermediary that translates mechanical deformation into optical pattern changes. The neural network serves as another intermediary that translates optical patterns into force information, enabling accurate multi-indenter detection without complex mechanical sensors.
3Measurement precision
If analytical force evaluation is used, then the calculation process is straightforward, but the force inference accuracy is limited
Solution Approach 1:
The patent performs preliminary training of the neural network using simulated force data and corresponding optical images. This pre-training phase creates a sophisticated evaluation model that can accurately infer forces from images during actual operation. The preliminary action of training transforms the system from simple analytical evaluation to intelligent pattern recognition, achieving high accuracy despite increased implementation complexity.
Solution Approach 2:
The patent creates a virtual copy of the measurement system through simulation. Synthetic force data and corresponding optical images are generated in a virtual environment to train the neural network. This copying approach allows the system to learn from extensive simulated scenarios without requiring equivalent physical test setups, achieving high inference accuracy while managing implementation complexity.
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
The method provides highly accurate and fine force inference, allowing the sensor arrangement to effectively measure forces applied on its surface, even when multiple indenters are present, enhancing robotic capabilities in object manipulation and movement.
Implementation Method 1
an elastically deformable wall, the wall comprising an outside measurement surface and an inside reflective surface
Implementation Method 2
a light source arrangement comprising several light sources being arranged to emit light towards the interior space
Data Source
AI summary
The disclosure relates to a method for force inference of a sensor arrangement using image data, to a corresponding training method for training a feed-forward neural network, to a corresponding force inference module and to a corresponding sensor arrangement.