Novel Calibration Methodologies for Compliant, Multiaxis, Fiber-Optic-Based Force/Torque Sensors
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Bibliographic record
Abstract
In this article, we present a number of novel calibration methodologies for compliant, multiaxis, fiber-optic-based force/torque sensors and evaluate the performance of these methods in real-time experiments with our custom-designed sensors. These methods address the challenges arising from the complex dynamic behavior of a compliant sensor and the nonlinearities in the force–deflection relationship. This article also investigates compliant sensor performance against its response characteristics, such as the impact of compliance on the sensor’s bandwidth using dynamic modeling and identification process. A powerful hysteresis compensation solution using a dynamic estimation model is also presented. Furthermore, we propose and apply a new calibration strategy building on the combination of a linear dynamic model combined with a static nonlinear model. This includes a state space (SS) model with Gaussian Process Regression (GPR) model named SSGPR. The results achieved from the proposed calibration method have revealed an improvement from an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> -squared value of 93.86%–100% when compared to data obtained using a linear dynamic model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it