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Record W2005242627 · doi:10.1177/0278364904044400

Synthesis of Reactionless Spatial 3-DoF and 6-DoF Mechanisms without Separate Counter-Rotations

2004· article· en· W2005242627 on OpenAlexafffund
Yangnian Wu, Clément Gosselin

Bibliographic record

VenueThe International Journal of Robotics Research · 2004
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLinkage (software)InertiaFour-bar linkagePlanarMechanism (biology)Rotation (mathematics)Bar (unit)Computer scienceControl theory (sociology)Tensor (intrinsic definition)Orientation (vector space)Motion (physics)GeometryMathematicsClassical mechanicsPhysicsArtificial intelligenceComputer graphics (images)Control (management)

Abstract

fetched live from OpenAlex

In this paper we present the synthesis of novel reactionless spatial three-degrees-of-freedom (3-DoF) and 6-DoF mechanisms without any separate counter-rotation, using four-bar linkages. Based on the conditions of dynamic balancing of a single planar four-bar linkage developed elsewhere, the spatial problem is shown to be equivalent to ensuring that the inertia tensor of reactionless four-bar linkage(s), which is(are) attached on the moving link of a reactionless four-bar linkage, remains constant while moving. The reactionless conditions for planar four-bar linkages undergoing spatial motion are first given. Then, reactionless spatial 3-DoF mechanisms using four-bar linkages are synthesized. A numerical example of the reactionless spatial 3-DoF mechanism is given and, with the help of the dynamic simulation software ADAMS, it is shown that the mechanism is reactionless for arbitrary trajectories. Finally, this mechanism is used to synthesize reactionless 6-DoF parallel mechanisms.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.329
Teacher spread0.293 · 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

Citations53
Published2004
Admission routes2
Has abstractyes

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