Stiffness Adaptation of a Hybrid Soft Surgical Robot for Improved Safety in Interventional Surgery
Bibliographic record
Abstract
Minimally invasive instruments are inserted per-cutaneously and are steered toward the desired anatomy. The low stiffness of instruments is an advantage; however, once the target is reached, the instrument usually is required to transmit force to the environment. The main limitation of the constant stiffness is predetermined maneuverability and cap of force transmission. Whereas, a highly flexible device can be safely steered through the body but is not suitable for payload limit, while a highly stiff device can have relatively high loads but cannot be steered in highly tortuous trajectories. To overcome this limitation, an adaptive stiffness soft robot was proposed, and the effects of the chamber pressure on the stiffness of the soft robot were investigated. To this end, a single-chamber pneumatic soft robot with one tendon was designed and fabricated. Afterward, a continuum mechanics model based on the nonlinear Cosserat rod model with hyperelastic material model and large deformation kinematics of the robot was developed. The shooting method solved the model as a boundary value problem with Dirichlet and Neumann boundary conditions. The results of the model showed stiffness adaptation feasibility with simultaneous tendon-driving and pneumatic actuation. Thus, to validate the theoretical findings, a series of experimental studies were performed with pressure in the range of 33 to 44 kPa and tendon tensions in the range of 0 to 2.7 N. The theoretical and experimental results for tip displacement and stiffness showed similar trends with a maximum error of 8.25%.
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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".