Pneumatic Hyperelastic Actuators for Grasping Curved Organic Objects
Bibliographic record
Abstract
Soft robotic grippers often incorporate pneumatically-driven actuators that can elastically deform to grasp delicate, curved organic objects with minimal surface damage. The complexity of the actuator geometry and the nonlinear stress–strain behavior of the stretchable material during inflation make it difficult to predict actuator performance prior to prototype fabrication. In this work, a scalable modular elastic air-driven actuator made from polydimethylsiloxane (PDMS) is developed for a mechanically compliant robotic gripper that grasps individual horticultural plants and fungi during automated harvesting. The key geometric design parameters include the expandable surface area and wall thickness of the deformable structure used to make contact with the target object. The impact of these parameters on actuator displacement is initially explored through simulation using the Mooney–Rivlin model of hyperelastic materials. In addition, several actuator prototypes with varying expandable wall thicknesses are fabricated using a multistep soft-lithography molding process and are inserted in a closed ring assembly for experimental testing. The gripper performance is evaluated in terms of contact force, contact area with the target, and maximum payload before slippage. The viability of the gripper with PDMS actuators for horticultural harvesting applications is illustrated by gently grasping a variety of mushroom caps.
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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) | 0.002 | 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".