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Record W3004172146 · doi:10.1115/1.4045935

Deflection Maps of Planar Elastic Catenary Cable-Driven Robots

2020· article· en· W3004172146 on OpenAlex
Leila Notash

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2020
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsCatenaryWorkspaceWrenchDeflection (physics)RobotPlanarStructural engineeringStaticsEngineeringComputer scienceMechanical engineeringPhysicsClassical mechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this paper, the cable tension and platform deflection of cable-driven robots are studied. The significance of cable density, elasticity, and cross-sectional area; platform mass, radius, and center of mass; the external wrench and platform orientation on the cable tension, platform deflection, and workspace of the planar cable robots is investigated. It is shown that, in addition to the cable mass, external wrench has a more prominent effect on the workspace of the catenary cable model. Moreover, design issues and parameters affecting the manipulator deflection are examined, and those that would result in disjointed workspace regions and deflection maps are identified. It is presented that the change in the deflection is gradual throughout the workspace for a constant external wrench. For the catenary model, depending on the cable properties, platform orientation, manipulator design, and external wrench, the workspace with the deflection limit may consist of disconnected regions.

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.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.069
Threshold uncertainty score0.582

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 head0.014
GPT teacher head0.195
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