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Record W2039269712 · doi:10.1115/1.2162870

A Compliant Contact Model Including Interference Geometry for Polyhedral Objects

2005· article· en· W2039269712 on OpenAlexaff
Lianzhen Luo, Meyer Nahon

Bibliographic record

VenueJournal of Computational and Nonlinear Dynamics · 2005
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsGeometric modelingGeometric shapeGeometric designRoboticsComputer scienceContact geometryGeometryInterference (communication)Contact forceRobotArtificial intelligenceSimulationMathematicsPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Modeling of contact with the environment is an essential capability for the simulation of space robotics system, which includes tasks such as berthing and docking. The effect of interbody contact on the robotic system has to be determined to predict potential problems in the design cycle. A compliant contact dynamics model is proposed here that considers most possible contact situations for a wide diversity of possible object shapes and using interference geometry information. A uniform formula is provided to determine the contact force as a function of geometric parameters and material properties. A corresponding geometric algorithm is provided in order to obtain the necessary geometric parameters. Some simulation results are presented based on the implementation of the geometric algorithm.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.255
Teacher spread0.237 · 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

Citations17
Published2005
Admission routes1
Has abstractyes

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