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Record W2168221389 · doi:10.5772/8705

Cartesian Control of a Cable-Driven Haptic Mechanism

2010· book-chapter· en· W2168221389 on OpenAlexafffund
Martin J.D., Vincent Duchaine, Greg Billette, Simon Perreault, Clément Gosselin, Denis Laurendeau

Bibliographic record

VenueInTech eBooks · 2010
Typebook-chapter
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyWorkspaceComputer scienceVirtual machineInterface (matter)InertiaMechanism (biology)SimulationTransmission (telecommunications)Virtual realityRobotEngineeringHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

Haptic devices operated through a communication network require a trade-off between the stability of the interaction and the quality of the haptic display. A haptic device must be designed to provide the best haptic display in order to reproduce the tactile sensation of virtual objects, rigid or soft, while ensuring a stable operation to guarantee user safety. The challenges are greater when considering a locomotion interface where a walker can produce large wrenches. A Cable-Driven Locomotion Interface, used as a peripheral in a virtual environment, is designed to address some of the aforementioned issues, since the use of cables as a mechanical transmission is known to provide many advantages such as low inertia, which is helpful in attaining high speeds and high accelerations, and the potential lengths of the cables can allow for large workspaces. Using this mechanism, a walker could navigate in a virtual environment with the aid of two haptic platforms (one for each foot) which can be regarded as two independent parallel robots constrained to six degrees of freedom and sharing a common workspace.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.194
Teacher spread0.183 · 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.

Study designBench or experimental
Domainnot available
GenreOther

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
Published2010
Admission routes2
Has abstractyes

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