Mechatronic design of haptic forceps for robotic surgery
Bibliographic record
Abstract
BACKGROUND: Haptic feedback increases operator performance and comfort during telerobotic manipulation. Feedback of grasping pressure is critical in many microsurgical tasks, yet no haptic interface for surgical tools is commercially available. METHODS: Literature on the psychophysics of touch was reviewed to define the spectrum of human touch perception and the fidelity requirements of an ideal haptic interface. Mechanical design and control literature was reviewed to translate the psychophysical requirements to engineering specification. High-fidelity haptic forceps were then developed through an iterative process between engineering and surgery. RESULTS: The forceps are a modular device that integrate with a haptic hand controller to add force feedback for tool actuation in telerobotic or virtual surgery. Their overall length is 153 mm and their mass is 125 g. A contact-free voice coil actuator generates force feedback at frequencies up to 800 Hz. Maximum force output is 6 N (2N continuous) and the force resolution is 4 mN. The forceps employ a contact-free magnetic position sensor as well as micro-machined accelerometers to measure opening/closing acceleration. Position resolution is 0.6 microm with 1.3 microm RMS noise. The forceps can simulate stiffness greater than 20N/mm or impedances smaller than 15 g with no noticeable haptic artifacts or friction. CONCLUSION: As telerobotic surgery evolves, haptics will play an increasingly important role.
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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) | 0.000 | 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 | 0.000 |
| 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".