Calibration Efficiency Analysis Based on Five Observability Indices and Two Calibration Models for a Six-Axis Industrial Robot
Bibliographic record
Abstract
The purpose of this work is to evaluate the efficiency of several calibration methods applied to a six-axis industrial (serial) robot. Specifically, the absolute position accuracy of a Fanuc LR Mate 200 iC industrial robot is improved using two calibration models. The first model is purely kinematic, and takes into account all geometric parameters. The second model considers, in addition, five compliance parameters related to the stiffness in joints 2, 3, 4, 5, and 6. For both models, the so-called calibration (or identification) robot configurations are selected based on an observability analysis. For each model, the efficiency of five different observability indices are compared. The parameter identification is based on the forward kinematic approach, where only the residual of the calibration positions is minimized. The data collection process is completely automated, by using Matlab, a FARO laser tracker, communication modules, and a special end-effector with three optical targets (SMRs). The comparisons of the observability indices and of the two calibration models are made through an exhaustive experimentation (7000 measurements). Results show that the robot's mean position error is reduced from 0.622 mm (before calibration) to 0.250 mm, when using the kinemetic model, and to 0.142 mm, when using the complete calibration model.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".