Visual servoing of continuum robots: Methods, challenges, and prospects
Bibliographic record
Abstract
BACKGROUND: Recent advancements in continuum robotics have accentuated developing efficient and stable controllers to handle shape deformation and compliance. The control of continuum robots (CRs) using physical sensors attached to the robot, particularly in confined spaces, is difficult due to their limited accuracy in three-dimensional deflections and challenging localisation. Therefore, using non-contact imaging sensors finds noticeable importance, particularly in medical scenarios. Accordingly, given the need for direct control of the robot tip and notable uncertainties in the kinematics and dynamics of CRs, many papers have focussed on the visual servoing (VS) of CRs in recent years. METHODS: The significance of this research towards safe human-robot interaction has fuelled our survey on the previous methods, current challenges, and future opportunities. RESULTS: Beginning with actuation modalities and modelling approaches, the paper investigates VS methods in medical and non-medical scenarios. CONCLUSIONS: Finally, challenges and prospects of VS for CRs are discussed, followed by concluding remarks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".