A fast torsionally compliant kinematic model of concentric-tube robots
Bibliographic record
Abstract
Concentric-tube robots have the potential to become an important surgical tool for robot-assisted percutaneous interventions. They can provide dexterous operation in a small constrained environment. The kinematic model of a concentric-tube robot has been well developed in terms of accuracy, but the computational cost places limitations on real-time implementation. In this paper, we propose a new technique that will substantially improve the computational efficiency of evaluating the kinematics of a concentric-tube robot in the context of developing a control strategy without sacrificing the accuracy of the results. In this paper we develop a torsionally compliant kinematic model using global variables. The model is validated by comparing the results obtained by computing the kinematic model corresponding to an experimental setup of a concentric-tube robot to which a force/torque sensor has been mounted at its base with those obtained directly from the experimental setup. The results indicate that it is feasible to compute the kinematics of the concentric-tube robot fast enough to allow the position/force control loop to be implemented at a rate of 1 kHz.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".