Finite Arc Method: Fast-Solution Extended Piecewise Constant Curvature Model of Soft Robots with Large Variable Curvature Deformations
Bibliographic record
Abstract
Accurate deformation modeling of soft flexural robots is of high practical importance, especially for high-risk tasks such as surgery. In this study, a new mechanistic model, that is, finite arc method (FAM), for soft robots, for example, tendon-drive, was proposed and validated. First, the catheter was modeled as a finite number of arcs, each with a constant bending curvature, hence the name FAM. Afterward, using a validated Bezier shape approximation, the deformation was parameterized, and the governing equations of the robot were derived. Also, a fast and recursive algorithm was proposed and implemented for the mechanical solution of the robot's deformation. To validate the proposed method, two validation studies were performed. In Study I, the FAM's predicted deformations for eight load cases in each two-dimensional and three-dimensional space on a 40 mm long flexure were compared with the nonlinear finite element method (FEM). In Study II, a representative set of lateral forces on a cardiac catheter (obtained in our previous study) was used to find its FAM-based deformation and was compared with the experimental reference. The error between FAM and FEM deformations was 0.23 ± 0.89 mm with computation times of 3 mseconds (FAM) versus 1244 mseconds (FEM). Also, the error of FAM compared with ground-truth in Study II was 1.41 ± 1.47 mm with a computation time of 7 mseconds. The proposed method showed acceptable performance for the accurate prediction of highly complex large deformations in real time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".