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Record W4386689542 · doi:10.1115/1.4063407

Exploiting the Kinematic Redundancy of a (6 + 3)-Degree-of-Freedom Parallel Manipulator to Produce Unlimited Rotation of the Platform

2023· article· en· W4386689542 on OpenAlexafffund
Arda Yiğit, David Le Breton, Clément Gosselin

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2023
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRevolute jointWorkspaceRedundancy (engineering)Parallel manipulatorKinematicsSingularityGravitational singularityRotation (mathematics)Control theory (sociology)PlanarRobotComputer scienceTopology (electrical circuits)MathematicsArtificial intelligenceGeometryMathematical analysisPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Abstract Mechanical interference and singularities within the reachable workspace often restrict the orientational workspace of parallel robots. Introducing kinematic redundancy can alleviate this limitation. This paper discusses the possibility to produce unlimited rotation of the platform of a tripedal (6 + 3)-degree-of-freedom kinematically redundant parallel robot. The articulated platform of such a robot has three degrees of mobility. The platforms considered here are planar linkages that contain either revolute or prismatic joints. It is shown that at least two revolute joints are required to produce unlimited rotation with appropriate design and initial configuration, while the platforms with two prismatic joints cannot produce such rotations without crossing a singularity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.233
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2023
Admission routes2
Has abstractyes

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