Simulation of a Kinematic Calibration Procedure that Employs the Relative Measurement Concept
Bibliographic record
Abstract
Presented in this paper is a project in which an autonomous camera-based calibration system is being developed.As with all other calibration methods, the desired goal is to improve the accuracy of a robot to the same degree asits repeatability. The distinct feature of this system is that it will employ the novel Relative Measurement Concept(RMC) to identify the discrepancies between nominal robot parameters, defined by its specified geometry, and theactual parameters defined by its manufacture. The geometry of a KUKA KR 15/2 was chosen for the simulation asa preliminary experiment was performed with this particular serial robot, however, substitution of other serial robotgeometries is possible. Derivation of the error model will be presented along with discussion on the components ofthe simulation. Program components include Pieper’s solution method to the inverse kinematic problem and SingularValue Decomposition (SVD). Results from the absolute measurement case and the relative measurement case, in itscurrent form, will be presented. The RMC method allows for the identification of 20 of the 24 robot parameters, in itspresent state, and will be experimentally validated with a Thermo CRS A465 six-axis serial robot.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".