A high expansion and contraction ratio tendon-driven spiral soft robotic arm inspired by the potato tower
Bibliographic record
Abstract
Tendon-driven soft robotic arms are widely used in unstructured environments due to their flexibility. However, most of the current tendon-driven soft robotic arms cannot achieve high expansion and contraction ratio and bending capabilities, which greatly limits their adaptability and operational flexibility. To address these issues, this study designed a tendon-driven spiral soft robotic arm with high expansion and contraction ratio and light weight inspired by the potato tower (the spiral body). With a compression ratio of 71% and a maximum bending angle of 180°, the spiral body structure can provide a larger workspace for the soft robotic arm. Its elasticity does not need to be provided by additional springs, so that the mass is only 72 g. In order to control its contraction and bending motion, the piecewise constant curvature method is used to establish a kinematic model and obtain a mapping relationship between the drive space and the task space, which is verified by experiment. The soft robotic arm also achieves the function of object collection and obstacle avoidance by utilizing this mapping relationship. Soft robotic arms with various end-effectors can be used for tasks such as pipe cleaning, emergency repairs and inspection maintenance in confined spaces.
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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".