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Record W4286499986 · doi:10.1115/1.4055062

Modeling and Analysis of Tendon-Driven Parallel Continuum Robots Under Constant Curvature and Pseudo-Rigid-Body Assumptions

2022· article· en· W4286499986 on OpenAlexaff
Kefei Wen, Jessica Burgner-Kahrs

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2022
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKinematicsJacobian matrix and determinantConstant curvatureSingularityCurvatureRigid bodyParallel manipulatorGravitational singularityRobotInverse kinematicsMathematicsRobot kinematicsClassical mechanicsMathematical analysisComputer scienceGeometryApplied mathematicsPhysicsArtificial intelligenceMobile robot

Abstract

fetched live from OpenAlex

Abstract This article unifies the approaches of kinematic and static modeling, and singularity analysis for tendon-driven parallel continuum robots under constant curvature as well as pseudo-rigid-body assumptions with those implemented in conventional rigid parallel robots. Constraint conditions are determined for the legs of this type of parallel continuum robots based on which the velocity equations and Jacobian matrices are derived. These are further exploited for inverse kinematic and singularity analysis. Static models for the robot as well as for each of the continuum links under pseudo-rigid-body assumption are derived. Finally, a simulation example is given to validate the kinematic models. It is shown that singularities can be determined using Grassmann line geometry or by detecting the numerical values of three performance indices.

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 score0.002
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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