Modeling and analysis of soft robotic fingers using the fin ray effect
Bibliographic record
Abstract
Soft grasping of random objects in unstructured environments has been a research topic of predilection both in academia and in industry because of its complexity but great practical relevance. However, accurate modeling of soft hands and fingers has proven a difficult challenge to tackle. Focusing on this issue, this article presents a detailed mathematical modeling and performance analysis of parallel grippers equipped with soft fingers taking advantage of the fin ray effect (FRE). The FRE, based on biomimetic principles, is most commonly found in the design of grasping soft fingers, but despite their popularity, finding a convenient model to assess the grasp capabilities of these fingers is challenging. This article aims at solving this issue by providing an analytic tool to better understand and ultimately design this type of soft fingers. First, a kinetostatic model of a general multi-crossbeam finger is established. This model will allow for a fast yet accurate estimation of the contact forces generated when the fingers grasp an arbitrarily shaped object. The obtained mathematical model will be subsequently validated by numerically to ensure the estimations of the overall grasp strength and individual contact forces are indeed accurate. Physical experiments conducted with 3D-printed fingers of the most common architecture of FRE fingers will also be presented and shown to support the proposed model. Finally, the impact of the relative stiffness between different areas of the fingers will be evaluated to provide insight into further refinement and optimization of these fingers.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".