Three‐dimensional localization of thin‐walled sheet metal parts for robotic assembly
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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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".