Calibration-based absolute localization of parts for multi-robot assembly
Bibliographic record
Abstract
In multi-robot assembly of parts, for successful mating, the grasped parts must be located with sufficiently small position and orientation errors so that assembly can be achieved. This paper describes a new approach for determining the absolute three-dimensional spatial location of parts grasped by robots during assembly. Through a combination of robot pose calibration and part-sensor calibration, the robot, used to grasp the part, is calibrated to accurately position and orient parts to a designated mating location. First, by employing a robot pose measurement system, the 6 DOF robot pose errors relative to a reference coordinate frame are compensated. Second, with the implementation of a part pose measurement, the 6 DOF part pose errors, relative to the robot tool frame, are estimated in real time. An experimental verification of the proposed methodology using a single FANUC S–110 robot manipulating an automotive sheet metal part is described.
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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 | 0.000 | 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".