A Novel Force Sensing Method Based on Stress Imaging Analysis
Bibliographic record
Abstract
This paper presents a new approach to the design of multi-axial force sensing elements. We show that it is possible to measure multiple forces by detecting stress components - namely, normal stress, shear stress, and torque - with a single sensing element. Multi-axis force-torque sensors have become popular in the field of robotics, because they provide valuable information to robots during physical interaction; but these sensors have posed a challenge to researchers during fabrication, as they typically require multiple uni-axial sensing elements scattered over the mechanical structure of the force-torque sensor. We solve this problem by accomplishing the same functionality with just one sensing element. Our sensing element is composed of layers of elastomer, with conductive electrodes integrated within the two sensitive layers. When a force is applied, some (or all) of the electrodes within each layer are compressed, changing the capacitance and providing stress images. A stress-imaging analysis, thus, allows us to reliably infer the applied force. In this paper, we describe the design, fabrication, and characterization of our triaxial sensing element. After constructing the prototype, we validate its performance using a series of experiments. The results demonstrate that our stress-imaging analysis method does indeed allow the measurement of multiple force components with a single sensing element.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".