Comparison of two calibration methods for a small industrial robot based on an optical CMM and a laser tracker
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY The absolute accuracy of a small industrial robot is improved using a 30-parameter calibration model. The error model takes into account a full kinematic calibration and five compliance parameters related to the stiffness in joints 2, 3, 4, 5, and 6. The linearization of the Jacobian is performed to iteratively find the modeled error parameters. Two coordinate measurement systems are used independently: a laser tracker and an optical CMM. An optimized end-effector is developed specifically for each measurement system. The robot is calibrated using fewer than 50 configurations and the calibration efficiency validated in 1000 configurations using either the laser tracker or the optical CMM. A telescopic ballbar is also used for validation. The results show that the optical CMM yields slightly better results, even when used with the simple triangular plate end-effector that was developed mainly for the laser tracker.
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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