Robot-assisted Active Catheter Insertion: Algorithms and Experiments
Bibliographic record
Abstract
Angioplasty is a frequently performed clinical procedure in which a catheter is inserted into a blood vessel under image guidance to open narrowed or blocked arteries and to allow normal blood flow to resume. This paper is concerned with the development of algorithms for a robot-assisted method for a more accurate, safer and more reliable approach for catheter insertion that can reduce the potential for injury to patients and radiation exposure or discomfort to clinicians. A force control algorithm is presented for a robot to control the force of insertion of a catheter and prevent the catheter from buckling or “bunching up” during insertion. In addition, the paper also describes a master—slave control strategy to precisely control the bending angle of the tip of an active catheter instrumented with Shape Memory Alloy (SMA) actuators. A novel model for SMAs and a robust H ∞ loop-shaping controller have been implemented to guarantee robust performance of the active catheter. The algorithms for catheter insertion developed in this paper may help to prevent damage to the epithelial cells of an artery and enable easier guidance of the catheter into appropriate branches. In addition, a robotics-based approach could make it possible for a clinician to remotely perform the insertion of the active catheter from a safe and comfortable environment, thereby reducing exposure to harmful X-ray radiation. Experimental results are presented to illustrate the performance of the algorithms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".