Bibliographic record
Abstract
Industrial robot calibration packages, such as ABB CalibWare, are merely 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 paper, a systematic approach 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 model is associated with the robot, while the second model is associated with the tooling. Once the robot is fully calibrated, the first error will be eliminated. Thus, our method is focused on the second error model. For this calibration, a self-calibration method is developed by using a calibration plate with multiple holes. Then, the tooling calibration model is formulated against the distance between the two holes. For measurements of the distances, a camera is mounted on the tooling system. To present the error mapping, a virtual kinematic link is proposed, which directly connects the camera to the tooling system. For error identification, a linear computational method is utilized to determine the tooling calibration parameters. The linear computational method is based on the Taylor expansion of the robotic tooling model. Once the error is identified, the next step is to implement the error in the tooling kinematic model. A case study is provided to demonstrate the effectiveness of the proposed method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".