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Record W2093625489 · doi:10.1088/0960-1317/21/6/065016

Image-based visual servoing through micropart reflection for the microassembly process

2011· article· en· W2093625489 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2011
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVisual servoingJacobian matrix and determinantComputer visionArtificial intelligenceProcess (computing)Position (finance)Feature (linguistics)Reflection (computer programming)Noise (video)Computer scienceImage (mathematics)Line (geometry)Mathematics

Abstract

fetched live from OpenAlex

This paper presents an image-based visual servoing algorithm to perform the microassembly process with an uncalibrated manipulator. The proposed algorithm requires only the use of the visual information from a single-vision camera to evaluate the unknown Jacobian matrix. Two methodologies were examined to estimate the Jacobian matrix on-line. Through monitoring the selected feature and the image reflection from the surface, the 3D position between the slot and the micropart can be evaluated successfully for the assembly process. Experimental results confirmed that the Jacobian matrix computed from both methods can evaluate the position with an accuracy of 3.6 µm initially. By using a proportional gain control, the position accuracy can be improved to within 1 µm. Measurement noise during the image acquisition is determined to be one of the root causes of the evaluation accuracy.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.601
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.268
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it