Bibliographic record
Abstract
Industrial robot calibration packages, such as ABB CalibWare, are developed only for robot calibration. As a result, the robotic tooling systems designed and fabricated by the user are often calibrated in an ad-hoc fashion. In this thesis, a systematic way for robotic tooling calibration is presented in order to overcome this problem. The idea is to include the tooling system as an extended body in the robot kinematic model, from which two error models are established. The first error model is associated with the robot, while the second error model is associated with the tooling. Once the robot is fully calibrated, the first error will be reduced to the required accuracy. Thus, the method is focused on the second error model. For the tool error calibration, two formulations were used. The first is a linear formulation based on conventional calibration as well as self-calibration methods while the second is a nonlinear formulation. The conventional linear formulation was extensively investigated and implemented while the self-calibration was proven to be inadequate for the tooling calibration. Moreover, the nonlinear formulation was demonstrated to be very effective and accurate through experimental result. The end-effector position estimation as well as the tool pose estimation were obtained using a 3D vision system as an off-line error measurement technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".