A Modular Soft Gripper with Combined Pneu-Net Actuators
Bibliographic record
Abstract
Soft Pneumatic-Network (Pneu-Net) Actuators (SPAs) have been used extensively in making soft grippers, due to their simple driving forms and large bending deformation. However, the capabilities of the regular SPAs in complex soft gripping application environments are alone insufficient. This work, thus, proposes a modular soft gripper that combines the functionalities of regular and herringbone actuators. The bending deformation characteristics of the two actuators under pneumatic pressures are verified by finite element (FE) simulations and experiments. The functional characteristics of the two actuators are investigated experimentally through a series of methods including the blocking force test, lifting test, grasping strength test, and suction force test. The experimental results show that the regular actuator has the advantages of greater longitudinal bending deformation and higher blocking force; while, the herringbone actuator has better lifting stability and grasping strength due to its conformal deformations both in longitudinal and transverse directions. In addition, the vacuum experiments demonstrate that the actuators can lift heavy plate-like objects through vacuum suction. Based on the functional behaviors of the two actuators, the proposed modular gripper is loaded onto automatic equipment, and the gripper is tested to hook, grasp, or lift various objects with different shapes, sizes, and weights. In essence, the modular and multi-functional characteristics of the design make it a promising candidate for relatively complex and advanced gripping applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".