MétaCan
Menu
Back to cohort
Record W1983451652 · doi:10.1115/esda2012-83001

Functional Work Space Estimation of a Robot Using Forward Kinematics, D-H Parameters, and Shape Analyses

2012· article· en· W1983451652 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsKinematicsRobotBoundary (topology)Disjoint setsRepresentation (politics)Orientation (vector space)Boundary representationRobot kinematicsComputer scienceEnvelope (radar)Robot end effectorSpace (punctuation)Computer visionArtificial intelligenceMathematicsGeometryMobile robotMathematical analysis

Abstract

fetched live from OpenAlex

The functional work space for a given orientation is a subset of the work envelope and is not intuitive to define for 6 axis industrial robots. A 2D boundary curve is derived for each desired end effector orientation and tool vector. This is done via a geometric analysis and using the Denavit-Hartenberg notation for the forward kinematic representation. The feasible region for all orientations is determined by the use of Boolean intersections. Disjoint regions may occur. Assessing these elements establishes the boundary limits for subsequent evaluation and optimization tasks. An ABB IRB 140 robot is used to highlight the methodology.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.462
Threshold uncertainty score0.262

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 head0.055
GPT teacher head0.274
Teacher spread0.219 · 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

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicManufacturing Process and OptimizationFrench-language works237,207