Feedback-linearization-based 3D needle steering in a Frenet-Serret frame using a reduced order bicycle model
Bibliographic record
Abstract
Robotics-assisted needle steering can enhance the performance of needle-based clinical procedures such as biopsy, brachytherapy, and drug delivery. We present an automated needle steering system capable of steering needles in 3D toward targets in tissue while avoiding anatomical obstacles. The system comprises a nonholonomic model of needle steering in tissue and a nonlinear controller for 3D trajectory tracking in soft tissue. First, a new reduced-order model of needle steering is presented. The proposed model is fully controllable and all the system states can be estimated on the fly. Next, the model is transformed to a local coordinate system using the Serret-Frenet formulation. By means of this transformation, the needle steering problem is converted to the regulation of the distance of the needle tip from a desired 3D trajectory. Finally, using the transformed model, a novel nonlinear controller is developed to steer the needle in 3D while avoiding anatomical obstacles. The control strategy is validated through simulations. The simulations indicate that the system is stable and can successfully follow a 3D trajectory. The results are promising, enabling future research in flexible needle path planning and control using the proposed reduced-order model and the controller.
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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) | 0.001 | 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 | 0.000 |
| 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".