MétaCan
Menu
Back to cohort
Record W2108257036 · doi:10.1109/icia.2007.4295780

Automated Microassembly Task Execution Using Vision-Based Feedback Control

2007· article· en· W2108257036 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersCMC Microsystems
KeywordsGRASPAutomationTask (project management)Process (computing)Computer scienceGrippersSMT placement equipmentMicroelectromechanical systemsControl engineeringController (irrigation)Position (finance)Fuzzy logicMachine visionFuzzy control systemControl systemRobotArtificial intelligenceEngineeringMechanical engineeringSystems engineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, we introduce a vision based feedback control system used in the automation of microassembly of MEMS devices. The proposed system permits the automatic grasping of microparts using a passive compliant microgripper, the first step towards achieving complete automation of the microassembly process. Vision- based relative position and microassembly force feedbacks are fused and supplied to a fuzzy logic controller that corrects the alignment of the microgripper and micropart and guides the system towards achieving a successful grasp. The performance of the system is investigated experimentally, and the experimental results are presented.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.437

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 head0.008
GPT teacher head0.270
Teacher spread0.262 · 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