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Record W3117563981 · doi:10.1115/detc2000/mech-14105

Classification of 6-SPS Parallel Manipulators According to Their Components

2000· article· en· W3117563981 on OpenAlexaff
Xianwen Kong, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsParallel manipulatorComponent (thermodynamics)KinematicsParallelQuadratic equationActuatorDisplacement (psychology)Computer scienceUnivariateDegree (music)Control theory (sociology)Topology (electrical circuits)MathematicsArtificial intelligenceRobotGeometryCombinatoricsMultivariate statisticsPhysics

Abstract

fetched live from OpenAlex

Abstract The complexity of the forward displacement analysis (FDA) of 6-SPS parallel manipulators1 varies to a great extent with the change of their geometric parameters. This paper presents a classification of the 6-SPS parallel manipulators according to their components. At first, we give the components for the 6-SPS parallel manipulator. A component refers to a part of the 6-SPS kinematic chain in which the number of actuators is equivalent to the degree of freedom. In addition to the commonly used rigid bodies, points and (straight) lines are also taken as elements of the components. Type synthesis of the 6-SPS parallel manipulators is then performed. The influence of the types of components on the maximal numbers of configurations and the degrees of the characteristic polynomials of the 6-SPS parallel manipulators is then revealed. The number of redundant sensors needed to reduce the FDA of 6-SPS parallel manipulators to the solution of several univariate quadratic equations in sequence based on the component method is also 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.

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 score0.305
Threshold uncertainty score0.304

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 head0.029
GPT teacher head0.219
Teacher spread0.189 · 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

Citations25
Published2000
Admission routes1
Has abstractyes

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