Origami-Inspired Soft Pneumatic Actuators: Generalization and Design Optimization
Bibliographic record
Abstract
Soft actuators are essential to soft robots and can also be used with rigid-bodied robots. This paper is focused on methods for improving the applicability of origami-inspired soft pneumatic actuators (OSPA). Our method for rapidly fabricating OSPA is shown to be capable of making a range of actuator sizes out of different materials. The largest OSPA has a force-to-weight ratio of 124, and can lift a 44 kg mass using a −85 kPa supply pressure. Experiments with a smaller OSPA demonstrate that it can perform 150,000 contraction/extension cycles while carrying a 2 kg mass with minimal degradation due to its materials and design. Compared to other OSPAs for which fatigue tests were reported, our accordion pattern OSPA has the best values of work-to-mass ratio, max. force, and fatigue life. A computationally efficient FEA-based constrained optimization method for maximizing an OSPA’s work output is then proposed. A 55% improvement in the work output was predicted, while validation experiments with OSPA prototypes showed a 53% improvement. While these improvement percentages are very similar, the values of the predicted stroke and work output are about 16% larger than the experimental values. The optimization requires only ~5 h to run on a common laptop.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".