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Record W3158927722 · doi:10.1115/1.4051062

Computation of the Available Force Set of a 3-<u>RP</u>RR Kinematically Redundant Planar Parallel Manipulator

2021· article· en· W3158927722 on OpenAlexaff
Marc Arsenault, Roger Boudreau, Scott Nokleby

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2021
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsOntario Tech UniversityUniversité de MonctonLaurentian University
Fundersnot available
KeywordsParallel manipulatorComputationManipulator (device)PlanarKinematicsSet (abstract data type)Computer scienceSimulationRobotClassical mechanicsPhysicsArtificial intelligenceComputer graphics (images)Algorithm

Abstract

fetched live from OpenAlex

Abstract An algorithm is developed to determine the available force set (AFS) of the 3-RPRR kinematically redundant planar parallel manipulator. The results of the algorithm are verified against a brute force approach and are found to yield exact results with significantly less computational time. The use of the AFS in a robot design context is illustrated through the analysis of two performance indices: the maximum pure force capable of being applied in any direction and the maximum pure force capable of being applied in a given direction. The algorithm is used to compute the AFS and the performance indices throughout the 3-RPRR robot’s workspace. The proposed methodology is a useful tool for the design and analysis of the 3-RPRR robot and could be adapted to other kinematically redundant planar parallel manipulators.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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