A product-of-exponential-based robot calibration method with optimal measurement configurations
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Bibliographic record
Abstract
This article presents a systematic and practical calibration method for an industrial robot to improve its absolute accuracy. The forward kinematics is established based on the global product-of-exponential formula considering some practical constraints. An enhanced partial pose measurement technique is used to construct the linearized error model with only position measurement. All the kinematic parameters are identified via the linear least-squared iteration. The end effector errors are compensated by an inverse Jacobian iteration algorithm in the robot joint space. To suppress the influences of the measurement error, an improved sequential forward floating search algorithm is proposed to select an optimal subset of configurations from a large pool of measured poses based on the D-Optimality. The proposed algorithm is verified via simulations. The calibration method is validated by experiments on an industrial robot, showing that the absolute accuracy of the robot is improved about 10 times under a statistics sense after the 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 | 0.001 | 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