Control of repulsive force in a virtual environment using an electrorheological haptic master for a surgical robot application
Bibliographic record
Abstract
This paper presents control performances of a new type of four-degrees-of-freedom (4-DOF) haptic master that can be used for robot-assisted minimally invasive surgery (RMIS). By adopting a controllable electrorheological (ER) fluid, the function of the proposed master is realized as a haptic feedback as well as remote manipulation. In order to verify the efficacy of the proposed master and method, an experiment is conducted with deformable objects featuring human organs. Since the use of real human organs is difficult for control due to high cost and moral hazard, an excellent alternative method, the virtual reality environment, is used for control in this work. In order to embody a human organ in the virtual space, the experiment adopts a volumetric deformable object represented by a shape-retaining chain linked (S-chain) model which has salient properties such as fast and realistic deformation of elastic objects. In haptic architecture for RMIS, the desired torque/force and desired position originating from the object of the virtual slave and operator of the haptic master are transferred to each other. In order to achieve the desired torque/force trajectories, a sliding mode controller (SMC) which is known to be robust to uncertainties is designed and empirically implemented. Tracking control performances for various torque/force trajectories from the virtual slave are evaluated and presented in the time domain.
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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 | 0.000 | 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".