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Record W4317886150 · doi:10.3389/frobt.2022.1082185

Singularity analysis of 3-DOF planar parallel continuum robots with constant curvature links

2023· article· en· W4317886150 on OpenAlexafffund
Sven Lilge, Kefei Wen, Jessica Burgner-Kahrs

Bibliographic record

VenueFrontiers in Robotics and AI · 2023
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSingularityJacobian matrix and determinantKinematicsPlanarGravitational singularityCurvatureComputer scienceParallel manipulatorTopology (electrical circuits)RobotConstant curvatureConstant (computer programming)Classical mechanicsMathematicsMathematical analysisPhysicsGeometryArtificial intelligenceApplied mathematicsCombinatoricsComputer graphics (images)

Abstract

fetched live from OpenAlex

This paper presents the singularity analysis of 3-DOF planar parallel continuum robots (PCR) with three identical legs. Each of the legs contains two passive conventional rigid 1-DOF joints and one actuated planar continuum link, which bends with a constant curvature. All possible PCR architectures featuring such legs are enumerated and the kinematic velocity equations are provided for each of them. Afterwards, a singularity analysis is conducted based on the obtained Jacobian matrices, providing a geometrical understanding of singularity occurences. It is shown that while loci and occurrences of type II singularities are mostly analogous to conventional parallel kinematic mechanisms (PKM), type I singularity occurences for the PCR studied in this work are quite different from conventional PKM and less geometrically intuitive. The study provided in this paper can promote further investigations on planar parallel continuum robots, such as structural design and control.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.217
Teacher spread0.210 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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