Relative hardness measurement of soft objects by a new fiber optic sensor
Bibliographic record
Abstract
The measurement of relative hardness of soft objects enables replication of human finger tactile perception capabilities. This ability has many applications not only in automation and robotics industry but also in many other areas such as aerospace and robotic surgery where a robotic tool interacts with a soft contact object. One of the practical examples of interaction between a solid robotic instrument and a soft contact object occurs during robotically-assisted minimally invasive surgery. Measuring the relative hardness of bio-tissue, while contacting the robotic instrument, helps the surgeons to perform this type of surgery more reliably. In the present work, a new optical sensor is proposed to measure the relative hardness of contact objects. In order to measure the hardness of a contact object, like a human finger, it is required to apply a small force/deformation to the object by a tactile sensor. Then, the applied force and resulting deformation should be recorded at certain points to enable the relative hardness measurement. In this work, force/deformation data for a contact object is recorded at certain points by the proposed optical sensor. Recorded data is used to measure the relative hardness of soft objects. Based on the proposed design, an experimental setup was developed and experimental tests were performed to measure the relative hardness of elastomeric materials. Experimental results verify the ability of the proposed optical sensor to measure the relative hardness of elastomeric samples.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".