Three‐dimensional localization of thin‐walled sheet metal parts for robotic assembly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article presents a technical demonstration of a system for determining the three‐dimensional spatial location of complexly shaped, thin‐walled sheet metal parts grasped by robots during assembly. For successful part assembly, the precise location of grasped parts (essential for successful mating of parts) must be achieved. A localization system is implemented to determine the accurate position and orientation of a sheet metal part that has been picked up by a robot from an arbitrary location. The proposed localization system employs a novel sensing method, utilizing laser‐based proximity and edge detectors, to extract the part feature data in real time. These geometrical feature data are incorporated into an existing localization algorithm, which is based on the singular value decomposition formulation of the part localization problem. The sensing method is particularly effective in measuring 3‐D feature geometry (i.e., thin edges) of sheet metal parts. An experimental single‐robot test bed has been developed to demonstrate the feasibility of the part localization concept for a single sheet metal part. The experimental results obtained from the test bed demonstrate that the system can be effectively used for the localization of thin‐walled sheet metal parts. © 2002 Wiley Periodicals, Inc.
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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 it