Relative posture-based kinematic calibration of a 6-RSS parallel robot by optical coordinate measurement machine
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Bibliographic record
Abstract
In this article, a relative posture-based algorithm is proposed to solve the kinematic calibration problem of a 6-RSS parallel robot using the optical coordinate measurement machine system. In the research, the relative posture of robot is estimated using the detected pose with respect to the sensor frame through several reflectors which are fixed on the robot end-effector. Based on the relative posture, a calibration algorithm is proposed to determine the optimal error parameters of the robot kinematic model and external parameters introduced by the optical sensor. This method considers both the position and orientation variations and does not need the accurate location information of the detection sensor. The simulation results validate the superiority of the algorithm by comparing with the classic implicit calibration method. And the experimental results demonstrate that the proposal relative posture-based algorithm using optical coordinate measurement machine is an implementable and effective method for the parallel robot calibration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it