Deformation Characteristics of Three-Dimensional Spiral Soft Actuator Driven by Water Hydraulics for Underwater Manipulator
Bibliographic record
Abstract
The emergence of bionic soft robots has led to an increased demand for bionic soft actuating ends. In this study, a three-dimensional spiral water hydraulic soft actuator (3D-SWHSA), inspired by the winding action of an elephant's trunk, is proposed to provide a more targeted soft actuator catching method. The 3D-SWHSA is composed of multiple bending and twisting units (BATUs), which can produce winding deformation after being pressed. By using the principles of virtual work and integrating the Yeoh 3rd order model, a predictive model for winding was established to investigate the bending and twisting characteristics of BATUs with varying structural parameters through finite element simulation. Following the selection of an optimal set of structural parameters for the 3D-SWHSA, its bending and deformation capabilities were simulated using finite element analysis and subsequently validated experimentally. To validate its flexibility, adaptability, and biocompatibility, successful catching experiments were conducted in both air and underwater environments. Underwater organisms, including organisms with soft appearance such as starfish and sea cucumbers, and organisms with hard shell, such as sea snails and crabs, can also be caught harmlessly. In cases where a single 3D-SWHSA is insufficient for capturing objects with unstable centers of gravity or when the capture range is exceeded, the double 3D-SWHSAs can be utilized for cooperative winding. This study affirms the great potential of 3D-SWHSA in diverse marine applications, including but not limited to marine exploration, fishing, and operations.
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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".