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Record W2019913969 · doi:10.1109/iros.2014.6942601

A dual-motor robot joint mechanism with epicyclic gear train

2014· article· en· W2019913969 on OpenAlexafffund
Vincent Babin, Clément Gosselin, Jean-François Allan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsHydro-QuébecUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanism (biology)Dual (grammatical number)RobotJoint (building)Computer scienceGear trainRobot kinematicsControl engineeringMobile robotAutomotive engineeringEngineeringArtificial intelligencePhysicsBacklashStructural engineering

Abstract

fetched live from OpenAlex

This paper presents the concept of a new robotic joint composed of two electric motors as inputs, an epicyclic gearing system for the transmission, and a single output. The proposed joint mechanism has a wider range of speed and torque performances comparatively to a traditional robot joint using a single motor and gearbox. The dynamic equations for the mechanical transmission system are given and a dual-motor joint mechanism is designed and prototyped to test this new concept of robotic joint. Also, the potential advantages of this joint concept for the design of manipulators for which a wide range of performances are desired are discussed. This work is motivated by the development of field robots designed for the operation and maintenance tasks in power distribution lines.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.193
Teacher spread0.176 · 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 designBench or experimental
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

Citations10
Published2014
Admission routes2
Has abstractyes

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