Performances of observability indices for industrial robot calibration
Bibliographic record
Abstract
This work presents a comparison of the five observability indices used for robot calibration. The comparison is realized in order to determine the most appropriate observability index, which allows for the best parameter identification of a calibrated robot, and therefore leading to the best improvement of the robot accuracy. In this study, the accuracy analysis is based on the robot end-effector errors, which are expressed in term of Euclidean errors. The parameter identification process is based on minimizing the residual of the position errors. The actual values of these positions are usually measured by an external measurement device and have measurement noise. The position residuals are calculated in all the calibration configurations, which are selected by using observability indices. An optimal set of configurations is the one reducing the impact of the measurement noise on the parameter identification efficacy. Our study is carried out for the calibration of four robots: two degrees of freedom (DOF) and 6-DOF serial robots, and 2-DOF and 3-DOF planar parallel robots. The comparison of the observability indices was achieved through a Monte Carlo simulation, using 100 different cases for each of the four robots considered. The position measurement noise was assumed to be within a range of ± 200 μm. Investigations led to conclude that there is a specific index that may be considered the best observability index for robot calibration. Finally, an experimental study has been applied to a LR Mate 200ic FANUC robot and confirms the simulated results.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".