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Record W2138956297 · doi:10.1109/cdc.2006.377163

Multi-rate Control Architectures for Dextrous Haptic Rendering in Cooperative Virtual Environments

2006· article· en· W2138956297 on OpenAlexafffund
Mahyar Fotoohi, Shahin Sirouspour, David W. Capson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyComputer scienceRendering (computer graphics)Virtual realityHuman–computer interactionComputer graphics (images)MultimediaSimulation

Abstract

fetched live from OpenAlex

This paper is concerned with haptic simulation in multi-user virtual environments in which the users can haptically interact in a shared virtual world from separate workstations over an Ethernet local-area Network (LAN). High-fidelity haptic rendering requires a minimum control update rate of 1000Hz which is beyond the capability of popular network protocols such as the UDP and TCP/IP. Consequently, a multi-rate control strategy is adopted in which local force-feedback loops are executed at higher rates than data packet transmission between the user workstations. Two control architectures, i.e. centralized and distributed, are presented and their stability margins are compared. Two methods for mathematical modelling and analysis of the proposed multi-rate haptic control systems are examined. Analytical and experimental results demonstrate that the distributed control architecture is superior to the centralized controller from performance and stability perspectives. This is confirmed through experiments with a dual-user dual-finger haptic rendering platform.

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: none
Teacher disagreement score0.635
Threshold uncertainty score0.516

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations8
Published2006
Admission routes2
Has abstractyes

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