3-D Path Planning Using Improved RRT* Algorithm for Robot-Assisted Flexible Needle Insertion in Multilayer Tissues
Bibliographic record
Abstract
In the field of minimally invasive surgery, flexible needles can avoid blood vessels and organs more flexibly compared to rigid needles. One of the main challenges when using flexible needles to reach lesions is planning a suitable path. Due to the non-holonomic characteristic of the flexible needle dynamics and the tissue deformation caused by the needle tip during the insertion, the accessibility and safety of the needle’s states need to be considered in the path planning stage. In this article, we propose an adaptable algorithm by improving the canonical rapidly exploring random trees* (RRT*) algorithm to compute a path for the flexible needle to reach targets in a layered tissue environment. The improved RRT* algorithm that addresses the motion constraints of the flexible needle renders the computed path comparatively smoother and optimal in some approximation sense. In the proposed algorithm, a strategy of adapting some of its parameters for different tissues during the insertion is developed, which improves the safety of surgeries. Moreover, the path cost used in the algorithm takes the potential fields of surrounding obstacles into account, which is used to deal with the influence of the local movement of tissues during the needle puncture process. Simulations are conducted to verify the effectiveness of the proposed algorithm. The results show that the improved RRT* algorithm generates a smooth and safe path which satisfies the motion constraints of the flexible needle in layered tissue environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".