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Record W2152870419 · doi:10.1002/rob.10117

NURBS to Avoid Boundary Orientation Poses in Serial Manipulators

2003· article· en· W2152870419 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 Robotic Systems · 2003
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsSerial manipulatorKinematicsOrientation (vector space)Motion (physics)Flexibility (engineering)Computer scienceSpline (mechanical)TrajectoryBoundary (topology)Computer visionArtificial intelligenceRobotAlgorithmControl theory (sociology)MathematicsGeometryEngineeringParallel manipulatorControl (management)Mathematical analysisMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A procedure to build NURBS motion interpolants to avoid boundary orientation poses for serial manipulator architectures is presented. As an example, the PUMA architecture was used. The procedure, which emerged from B‐spline curves theory, enables a local change of the NURBS motion interpolant. The change may be introduced in any arbitrary neighborhood of the chosen boundary orientation pose. Therefore, when tracking a trajectory, one may change NURBS motion interpolant value at any time instant, leaving its remaining values untouched. Recommendation for further research pertains to exploiting the flexibility of NURBS applied to robot kinematics. © 2003 Wiley Periodicals, Inc.

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 score0.660
Threshold uncertainty score0.469

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 head0.011
GPT teacher head0.219
Teacher spread0.209 · 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