Development of a Stick-Slip Dielectric Elastomer Actuator for Robotic Applications
Bibliographic record
Abstract
Dielectric elastomer actuators (DEAs) face a performance tradeoff between achieving large displacements and high driving speeds, which limits their use in precision actuation scenarios requiring both rapid response and a wide motion range. To address these limitations, this study introduces a novel stick-slip actuation strategy that leverages the unique deformation properties of dielectric elastomers (DEs) combined with friction interface dynamics. Based on this principle, a stick-slip dielectric elastomer actuator (SSDEA) was developed, consisting of a DEA module with two fan-shaped electrode regions, a spherical rotor, and a 3D-printed framework. The structural design simplifies assembly by eliminating the need for complex techniques while ensuring operational stability. This design enables continuous multi-degree-of-freedom (multi-DOF) rotational motion without traditional transmission mechanisms. A dynamic model is established to analyze the actuator's behavior, and experimental validation confirms that the stick-slip strategy enhances both motion speed and resolution compared to conventional DEA systems. Its integration into a robotic facial model for simulating eyeball movements demonstrates its potential for compact, high-performance actuation in humanoid robotics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".