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Transferability in an 8-DoF Parallel Robot with a Configurable Platform

2020· article· en· W3131970116 on OpenAlexafffund
Redwan Dahmouche, Kefei Wen, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsWrenchParallel manipulatorTransferabilityRobotScrew theoryKinematicsComputer scienceConstruct (python library)Robot kinematicsKinematic chainStewart platformControl engineeringSimulationEngineeringMobile robotArtificial intelligenceMechanical engineeringProgramming language

Abstract

fetched live from OpenAlex

Parallel robots with configurable platforms (PRCPs) combine the benefits of parallel robots with additional functionalities such as grasping and cutting. However, some of the theoretical tools used to study classical parallel robots do not apply to parallel robots with configurable platforms. This paper uses screw theory to study the transferable wrenches from the robot's limbs to the configurable platform of an 8-DoF parallel robot. Deriving the transferable wrenches allows one to construct the screw system that is applied to each part of the configurable platform. Based on the analytical expressions of the limb and platform wrenches that have been derived and numerically validated, the mathematical tools that are used to study parallel kinematic structures, such as Grassmann line geometry, can thus be applied to the presented parallel robot with a configurable platform.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.200
Teacher spread0.174 · 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
Published2020
Admission routes2
Has abstractyes

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