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Record W2800608656 · doi:10.1139/tcsme-2014-0015

DYNAMICS OF TWO ACTIVE AUTONOMOUS DOCK MECHANISMS FOR AUV RECOVERY

2014· article· en· W2800608656 on OpenAlexaffvenueabout
Jason Currie, Colin B. Gillis, Juan A. Carretero, Rickey Dubay, Tiger Jeans, George D. Watt

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDefence Research and Development CanadaUniversity of New Brunswick
Fundersnot available
KeywordsSubmarineUnderwaterMarine engineeringTrajectoryComputer scienceHeading (navigation)Revolute jointRange (aeronautics)EngineeringControl theory (sociology)RobotGeologyAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Autonomous Underwater Vehicles (AUVs) are presenting an ever expanding range of applications that enhance human capabilities and mitigate human risk. Development of a successful subsurface autonomous launch and recovery system would expand the functional use of AUVs in many fields, e.g., year-round Canadian Arctic exploration and sovereignty missions. This paper provides an overview of the design and dynamic modelling of two concept mechanisms being developed to recover AUVs to a slowly moving submerged submarine. Both have a serial R⊥R⊥P architecture; one is mechanically actuated while the second uses an actively pitched wing to indirectly provide motive force for the passive revolute joint. Dynamic models of both manipulators are developed. Although similar in architecture, several extensions are required to accurately predict the non-linear dynamics provided by the wing. High speed actuation of the devices is required to compensate for relative trajectory errors between the submarine and AUV during significant sea states in littoral waters. Alterations to the recursive Newton–Euler method to include hydrodynamic and additional inertial forces present in water are explained. Results of some initial modelling are presented.

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: Methods · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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
GenreMethods

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

Citations11
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicUnderwater Vehicles and Communication SystemsFrench-language works237,207