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Record W2055900711 · doi:10.1115/detc2007-35531

Type Synthesis of Six-DOF Wrist-Partitioned Fully Parallel Manipulators

2007· article· en· W2055900711 on OpenAlexafffund
Xianwen Kong, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsParallel manipulatorKinematicsComputer scienceScrew theoryWristSerial manipulatorSingularityControl theory (sociology)Orientation (vector space)Position (finance)Displacement (psychology)Stewart platformArtificial intelligenceMathematicsRobotControl (management)Geometry

Abstract

fetched live from OpenAlex

A six-DOF wrist-partitioned fully parallel manipulator is a parallel manipulator in which three of the six actuated joints are used to control the position of a point on the moving platform while the other three are further used to control the orientation of the moving platform. Such parallel manipulators are in fact the parallel counterparts of the wrist-partitioned serial manipulators, which are widely used in industry. Unlike parallel manipulators of a general structure, a six-DOF wrist-partitioned fully parallel manipulator usually has simple kinematic characteristics such as its forward displacement analysis and singularity analysis are easy to solve. This paper deals with the type synthesis of six-DOF wrist-partitioned fully parallel manipulators. An approach is first proposed for the type synthesis of this class of parallel manipulators. Using the proposed approach, six-DOF wrist-partitioned fully parallel manipulators can be constructed from the types of three-DOF non-overconstrained spherical parallel manipulators. A large number of six-DOF wrist-partitioned fully parallel manipulators are then obtained, and several types of practical relevance are also identified.

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: none
Teacher disagreement score0.822
Threshold uncertainty score0.464

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 head0.210
Teacher spread0.199 · 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

Citations11
Published2007
Admission routes2
Has abstractyes

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