Design of a Durable Air-Muscle With Integrated Sensor for Soft Robotics
Bibliographic record
Abstract
Soft robotics integrates compliant actuators and sensors that expand design possibilities beyond classic robotics based on rigid modular components. In particular, deformable elastomer-based actuators used in soft robots, such as air-muscles, offer the possibility of having large numbers of embedded degrees of freedom. However, air-muscles fatigue life and strain capability call for a tradeoff, limiting their practical use in demanding applications such as physical rehabilitation, medical robotics, and mobile robots. This paper presents the design of a durable high-strain air-muscle composed of a silicone tube and an axially elastic sleeve (radially rigid), which integrates a flexible Dielectric Elastomer (DE) position sensor. The uniformity of the sleeve, by opposition to usual braids, makes for a reinforcement without local stresses that cause membrane failure. Designed based on fatigue failure principles, this air-muscle withstands 145 000 cycles at 50 % elongation, which demonstrates its potential as a durable high-strain actuator. Performance maps of the air-muscle confirm good linearity between force, pressure and strain and demonstrate bi-directional force capability. Furthermore, the integration of a DE sensor allows for accurate position control of the air-muscle (0.17 mm), making the air-muscle/sensor unit a relevant building block for complex soft robotics systems. The all-polymer high-strain actuator/sensor unit proves to be accurate and durable as well as cost-effective, thus making it ideal for soft robotics applications requiring large numbers of actuators and integrated sensing.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".