Design and Calibration of a Soft Multiple Degree of Freedom Motion Sensor System Based On Dielectric Elastomers
Bibliographic record
Abstract
Soft robots use active deformable structures to provide highly capable yet simple and robust robotic systems. Motion sensors for soft robots must, therefore, be able to provide joint position sensing on deformable, multiple degrees of freedom (DOFs) joints often found in soft robot architectures and whose kinematics are not accurately described by closed-form mathematical models. This article proposes a method for designing dielectric elastomer sensor systems for such soft robots. The method is presented as a case study of a soft sensor system for an existing robotic manipulator designed for magnetic resonance image-guided surgery to the prostate. A calibration method based on support vector regression (SVR) is proposed to calibrate the coupled, multi-DOFs sensor system without a model. A prototype sensor system is built and is shown to reach a precision of 0.3 mm root mean square/1.2 mm maximum when calibrated with SVR. These results show sufficient precision for many applications and suggest that model-free calibration is a viable technology for soft robots.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".