Vision-based Autonomous Blood Suction with a Concentric Tube Continuum Robot
Bibliographic record
Abstract
Blood-water mixture removal is essential in many surgeries. Existing works adding a robotic assistant primarily focus on conventional robots. Limited attention has been given to using continuum robots for this task. This paper introduces a vision-based control framework for autonomous liquid suction using a concentric tube continuum robot (CTCR). The proposed method employs a controller using camera input combined with a hybrid control strategy that integrates differential inverse kinematics and a pre-computed lookup table to ensure stable and precise motion during suction. A CTCR simulator, implemented in the Unity Game Engine with photorealistic rendering and robot-liquid interaction capabilities, as well as a benchtop robot system were developed as the experiment platform. The proposed method was evaluated through simulation and real-world experiments across four scenarios, demonstrating its generalizability and stability. In 32 real-world trials, less than 0.1 g of liquid remained after the suction, while over 99% of liquid was removed across 32 simulated trials. The results highlight the potential of CTCR for autonomous surgical liquid suction, showcasing the system’s adaptability and performance in dynamic environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".