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Record W2156803342 · doi:10.1109/robot.2009.5152182

Interference estimated time of arrival on a 6-DOF cable-driven haptic foot platform

2009· article· en· W2156803342 on OpenAlexafffund
Martin J.-D. Otis, Thien-Ly Nguyen-Dang, Denis Laurendeau, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsWorkspaceHaptic technologyClassification of discontinuitiesInterference (communication)Computer scienceSimulationRange (aeronautics)Interface (matter)Task (project management)AccelerationEngineeringRobotArtificial intelligenceTelecommunicationsAerospace engineering

Abstract

fetched live from OpenAlex

A Cable-Driven Locomotion Interface employs two independent cable-driven haptic foot platforms constrained in six degrees of freedom (6-DOF). Its control system and its geometry are designed for performing a wide range of trajectories that could generate cable interferences. This paper presents and analyzes computational methods for determining which cable can be released from an active actuation state while allowing control in a minimal tension state, thereby ensuring that both platforms stay in a controllable workspace. One challaging task is to develop light and fast computational algorithms for hard real time processes included in haptic display applications. Seeing that releasing a cable from an active actuation state might generate discontinuities in tension values in the other cables, this paper proposes collision prediction schemes named Interference Estimated Time of Arrival in order to reduce or completely eliminate such discontinuities.

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 score0.458
Threshold uncertainty score0.759

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)0.0010.000

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 head0.026
GPT teacher head0.236
Teacher spread0.210 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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