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Record W2797466960 · doi:10.1115/1.4039962

Combined Effect of Sampling and Coulomb Friction on Haptic Systems Dynamics

2018· article· en· W2797466960 on OpenAlexaff
Csaba Budai, László L. Kovács, József Kövecses

Bibliographic record

VenueJournal of Computational and Nonlinear Dynamics · 2018
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoulombHaptic technologyCoulomb frictionCollision responseDissipationDynamical frictionDissipative systemDiscontinuity (linguistics)SimulationComputer scienceCoulomb's lawMechanicsClassical mechanicsControl theory (sociology)PhysicsNonlinear systemArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Dissipation mechanisms and dissipative forces play a pivotal role in the operations and performance of human-machine interfaces and particularly in haptic systems. Dissipation is a very difficult phenomenon to model. Coulomb friction in general can be the most influential element in systems involving multiple direct contact connections such as joints with transmissions or mechanically guided components. Coulomb friction includes nonsmooth discontinuity and can induce complex dynamic behaviors. The effect of Coulomb friction is often neglected in haptics. The part of the literature which deals with friction mainly focuses on friction compensation and/or simulation of friction for haptic rendering. In this paper, the nature of the dynamic behavior caused by Coulomb friction in haptic sampled-data systems is illustrated by experiment, analysis, and simulation. It is also demonstrated that a simple model can represent this behavior and show the effects of the haptic system parameters on this dynamics.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.355

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 head0.008
GPT teacher head0.237
Teacher spread0.230 · 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
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

Citations5
Published2018
Admission routes1
Has abstractyes

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