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Record W2095447975 · doi:10.1115/detc2010-28829

Completely Eliminating the Singularities of a 3-DOF Planar Parallel Robot With Only One Degree of Actuation Redundancy

2010· article· en· W2095447975 on OpenAlexaff
Novona Rakotomanga, Ilian A. Bonev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsRedundancy (engineering)Gravitational singularityCartesian coordinate systemSingularityWorkspaceParallel manipulatorTopology (electrical circuits)RobotManifold (fluid mechanics)ActuatorPlanarMathematicsDimension (graph theory)Computer scienceEquilateral triangleControl theory (sociology)Pure mathematicsMathematical analysisGeometryArtificial intelligenceCombinatoricsEngineeringComputer graphics (images)Mechanical engineering

Abstract

fetched live from OpenAlex

The Cartesian workspace of an n-DOF parallel robot (n < 6) is generally divided by singularity hyper-surfaces of dimension n−1. A common approach to reducing the dimension of the singularity manifold is to use actuation redundancy. However, in all previously reported works, adding one redundant actuator reduces the dimension of the singularity manifold by only one. This paper is the first to demonstrate that a properly designed actuation redundancy can be much more effective than this. Specifically, a 3-RPR design is presented in which the mobile platform and the base are equilateral triangles and show that adding a single RPR leg connecting the centers of these two triangles completely eliminates the singularities of the robot, which are otherwise a surface in the Cartesian space.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.207
Teacher spread0.181 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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