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Record W3108169832 · doi:10.1109/tro.2020.3038697

Wrench Feasibility and Workspace Expansion of Planar Cable-Driven Parallel Robots by a Novel Passive Counterbalancing Mechanism

2020· article· en· W3108169832 on OpenAlexaff
Hamed Jamshidifar, Amir Khajepour, Amin Habibnejad Korayem

Bibliographic record

VenueIEEE Transactions on Robotics · 2020
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWrenchWorkspaceFootprintActuatorPlanarControl theory (sociology)Mechanism (biology)RobotParallel manipulatorComputer sciencePoint (geometry)Topology (electrical circuits)MathematicsEngineeringMechanical engineeringGeometryArtificial intelligencePhysicsGeology

Abstract

fetched live from OpenAlex

This article focuses on the essential limitations of planar point-mass cable-driven parallel robots (CDPRs) in covering all poses of their footprint, which results in concave-shape static workspaces (SW) and also providing a zero force level on the borders of such a SW. Accordingly, a novel passive counterbalancing mechanism is proposed which not only expands SW to fully cover the footprint but also enables CDPR's platform to balance a desired minimum force magnitude in any arbitrary direction all over the SW. Maximizing such force magnitude is defined as an optimization problem which is used to find the optimal values of the proposed mechanism's design parameters. By comparing the SW of different CDPRs with and without the proposed mechanism, effectiveness of the proposed approach is demonstrated. In some examples, it is shown that the effects of the proposed method on the SW size increment is more than doubling the size and number of actuators. Finally, two experimental setups are presented and tested, where effectiveness of the proposed approach in covering the CDPRs’ footprint and also providing the desired minimum force level over the SW are demonstrated.

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 categoriesMeta-epidemiology (narrow)
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 score0.449
Threshold uncertainty score1.000

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 head0.020
GPT teacher head0.213
Teacher spread0.192 · 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.

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

Citations25
Published2020
Admission routes1
Has abstractyes

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