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Record W1979560700 · doi:10.1115/detc2014-35082

Symbolic Formulation of a Path-Following Joint for Multibody Dynamics

2014· article· en· W1979560700 on OpenAlexafffund
Andrew J. Hall, Chad Schmitke, John McPhee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsKinematicsParameterized complexityMultibody systemPath (computing)MathematicsOrdinary differential equationSpline (mechanical)Motion planningReduction (mathematics)PlanarComputer scienceDifferential equationMathematical analysisAlgorithmGeometryArtificial intelligenceClassical mechanicsRobotPhysics

Abstract

fetched live from OpenAlex

We present a specialized multibody joint that constrains motion to a spatial path. The joint is used in the reduction of 1 degree-of-freedom systems with complex kinematics. Example applications of the joint are: the reduction of vehicle suspension systems, or the representation of biological joints. The new joint is implemented in the graph-theoretic symbolic multibody modeling environment of MapleSim and is formulated in such a way that a single ordinary differential equation is used to describe the resulting kinematic pair. A particle moving along a planar semi-circular path was chosen as the first example for successful validation of the new joint since a simple closed-form solution in terms of the path length exists. To represent arbitrary curves, the path must first be parameterized in terms of its path length. Next, a differentiable mathematical definition of the curve must be generated. B-splines are generated to define the path. For best performance we minimize the number of knots in the splines and find their optimal locations. Using the spline fitting approach, a planar parabolic path is generated and used to further analyze the performance of our implementation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score0.282

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2014
Admission routes2
Has abstractyes

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