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Record W4293192926 · doi:10.1115/detc2022-89384

Design and Kinetostatic Modeling of a Cable-Driven Schönflies-Motion Generator

2022· preprint· en· W4293192926 on OpenAlexaff
G Sciarra, Tahir Rasheed, Valentina Mattioni, Philippe Cardou, Stéphane Caro

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsWorkspaceParallelogramActuatorGenerator (circuit theory)Parallel manipulatorMechanism (biology)Computer scienceRotation (mathematics)Control theory (sociology)EngineeringRobotPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Cable-Driven Parallel Robots (CDPRs) use cables to move a moving-platform in space offering different advantages, such as high payload, reconfigurability, large translational workspace and high dynamic performances. However, their orientational workspace is usually limited due to cable/cable and cable/moving-platform collisions. In this paper, a novel Schönflies-Motion Generator (SMG) with a large translational workspace and a full rotation of its end-effector about a vertical axis is introduced. The full rotation of the end-effector is obtained using a parallelogram cable loop. It should be noted that the four degrees of freedom motion of the end-effector is controlled by four actuators fixed to the ground thanks to three parallelogram cable loops and a transmission system containing a differential mechanism. The kinetostatic model of the CDPR under design is expressed. The static workspace of the proposed Cable-Driven SMG (CDSMG) with three different cable arrangements is analyzed. Finally, a prototype of the CDSMG is presented and preliminary experimental results are discussed.

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 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 score0.121
Threshold uncertainty score0.804

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 head0.029
GPT teacher head0.220
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2022
Admission routes1
Has abstractyes

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