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Record W3114435022 · doi:10.1115/detc2000/mech-14094

Determination of the Uniqueness Domains of 3-RPR Planar Parallel Manipulators With Similar Platforms

2000· article· en· W3114435022 on OpenAlexaff
Xianwen Kong, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsUniquenessSingularityWorkspaceDomain (mathematical analysis)Parallel manipulatorPlanarDisplacement (psychology)Serial manipulatorCartesian coordinate systemManipulator (device)Computer scienceExpression (computer science)MathematicsTopology (electrical circuits)Control theory (sociology)Mathematical analysisRobotArtificial intelligenceGeometryCombinatoricsControl (management)

Abstract

fetched live from OpenAlex

Abstract A uniqueness domain is a part of the Cartesian workspace corresponding to the same assembly mode of the 3-RPR (planar parallel) manipulator. This paper presents an efficient method to determine the uniqueness domains of the 3-RPR manipulators with similar platforms. The method is based on the singularity and forward displacement analysis (FDA) of the 3-RPR manipulator with similar platforms. The singularity analysis and the FDA of the 3-RPR manipulator with similar platforms is first performed. It is then proved that each of the solutions distributes into different singularity-free regions of the manipulator. Each singularity-free region corresponds to one uniqueness domain of the 3-RPR manipulator with similar platforms which can thus be determined in a direct way. At last, it is proved that the four solutions in analytic expression form to the forward displacement analysis correspond to different uniqueness domains for the 3-RPR manipulator with similar aligned platforms. This simplifies further the FDA in this case as the unique solution to the FDA can be found without the need to compute all the four solutions as long as the singularity-free region in which the manipulator works is given.

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.

How this classification was reachedexpand

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: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.193

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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2000
Admission routes1
Has abstractyes

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