Dynamical modeling and controllability analysis of a flexible needle in soft tissue
Bibliographic record
Abstract
This paper is concerned with deriving a dynamic model of a moderately flexible needle inserted into soft tissue, where the model's output is the needle deflection. The main advantages of the proposed dynamic modeling approach are that the presented model structure involves parameters that are all measurable or identifiable by simple experiments and that it considers the same inputs that are currently used in the clinical practice of manual needle insertion. Conventional manual needle insertion suffers from the fact that flexible needles bend during insertion and their trajectories often vary from those planned, resulting in positioning errors. Enhancement of needle insertion accuracy via robot-assisted needle steering has received significant attention in the past decade. A common assumption in previous research has been that the needle behavior during insertion can be adequately described by static models relating the needle's forces and torques to its deflection. For closed-loop control purposes, however, a dynamic model of the flexible needle in soft tissue is desired. In this paper, we propose a Lagrangian-based dynamic model for the coupled needle/tissue system, and analyze the response of the dynamic system. Steerability (controllability) analysis is also performed, which is only possible with a dynamic model. The proposed dynamic model can serve as a cornerstone of future research into designing dynamics-based control strategies for closed-loop needle steering in soft tissue aimed at minimizing position error.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".