An efficient method of correcting position mismatch between a haptic device and a robot-assisted tool
Bibliographic record
Abstract
In robot-assisted surgery, a critical factor is to handle pose (position and orientation) mismatch between a stylus-style hand controller and a surgical tool (robot-assisted tool) attached to the end-effector of a robot. The mismatch has similar characteristics as robot calibration in industrial settings. Nevertheless, any methods for correcting the mismatch need to meet certain computation and accuracy requirements, which are derived from the constraints of a robot-assisted surgical system. On a virtual reality simulator, we use a haptic device (PHANToM Premium 1.5/6DOF) as the hand controller to actuate a robot-assisted surgical tool via neuroArm - a robotic system in microscopic neurosurgery. Within the workspace of the tool, we have defined the computation and accuracy requirements of correction as 1.0 ms and 30.0 μm, respectively. Towards the correction of the pose mismatch, this current work first assesses the suitability of the Newton-Raphson (NR) method for addressing the position mismatch between the haptic interface point (HIP) of the haptic device and the tooltip of the surgical tool. For fast computation, we have modified the NR method to take advantage of its quadratic rate of convergence. This modification adds a feedback loop for selecting appropriate initial values. As well, we have verified the non-singularities of the workspace where the position mismatch needs to be corrected. Assessed in the workspace of 90 targets, the modified NR method achieves an accuracy between the HIP and the tooltip at about 1.0 μm in less than 64 μs - meeting both requirements of correction. Thus, this work confirms the suitability of the modified NR method to efficiently correct the position mismatch between the HIP and the tooltip on neuroArm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".