Manipulability of teleoperated surgical robots with application in design of master/slave manipulators
Bibliographic record
Abstract
Teleoperated surgical robots can significantly improve the performance of minimally invasive surgeries. The performance of a master-slave robotic system depends significantly on the capability of its master device to appropriately interface the user with the slave robot. However, master robots currently used in the clinic present several drawbacks such as the mismatch between the slave and master workspaces and the inability to intuitively transfer the slave robot's dexterity and joint limits to the user. In this paper, the "teleoperation manipulability index (TMI)" is introduced as a quantifiable measure of the combined master-slave system manipulability. We also demonstrate the application of the TMI in the design of master-slave robotic systems. By employing the proposed manipulability index, we are able to modify the design of a commercially available master robot that 1) enhances surgeon's control over force/velocity of a surgical robot, 2) minimizes the master robot's footprint, 3) optimizes the surgeons' control effort, and 4) avoids singularities and joint limits of the master and slave robots. A simulation study is performed to validate the performance of the modified master-slave robotic system.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".