Bibliographic record
Abstract
The goal of the present study is to improve the accuracy of six-revolute industrial robots using calibration methods. These methods identify the values of the calibrated robot model to improve the correspondence between the real robot and the mathematical model used in its controller. The calibrated robot model adds error parameters to the nominal model, which correspond to the geometric errors of the robot as well as the stiffness behavior of the robot. \n \nThe developed methods focus on using low cost measurement equipment. For instance, the first work makes a comparison between a robot calibration performed using a laser tracker and a stereo camera (MMT optique) separately. The accuracy performance is validated using a telescoping ballbar for each of the two methods. While the calibration result is the same for both methods, the price of a laser tracker is more than twice the price of a stereo camera. The method is tested using an ABB IRB120 robot, a Faro ION laser tracker, and a Creaform CTrack stereo camera to calibrate the robot. A Renishaw QC20-W ballbar is used to validate the accuracy. \n \nA novel measurement system to measure a set of poses is described in the second work. The device is an extension of a known approach using an hexapod (a Stewart-Gough platform). One fixture is attached to the robot base and the other to the robot end-effector, each having three magnetic cups. By taking six ballbar measurements at a time, it is possible to measure 144 poses of the triangular fixture attached to the robot end-effector with respect to the base fixture. The position accuracy of the device is 3.2 times the accuracy of the QC20-W ballbar: ± 0.003 mm. An absolute robot calibration using this novel 6D measurement system is performed in the third work of this thesis. The robot is calibrated in 61 configurations and the absolute position accuracy of the robot after calibration is validated with a Faro laser tracker in about 10,000 robot configurations. The mean distance error is improved from 1.062 mm to 0.400 mm in 50 million pairs of measurements throughout the complete robot workspace. To allow a comparison, the robot is also calibrated using the laser tracker and the robot accuracy validated in the same 10,000 robot configurations.
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How this classification was reachedexpand
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".