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Record W3173849926 · doi:10.18280/jesa.540304

Representation of New Variable Thrust Vector Underwater Robotic Platform for Complex Trajectory Tracking

2021· article· fr· W3173849926 on OpenAlexvenueno aff
Ali Kadkhodaei, Reza Hasanzadeh Ghasemi

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2021
Typearticle
Languagefr
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTrajectoryVariable (mathematics)Representation (politics)Tracking (education)UnderwaterThrustArtificial intelligenceComputer scienceComputer visionControl theory (sociology)EngineeringMathematicsGeologyAerospace engineeringPsychologyPhysicsControl (management)

Abstract

fetched live from OpenAlex

Underwater robots are integral parts of the marine industry and science. The application of the underwater vehicles has increased with the development of the activities in deep sea. This paper considers the dynamics modeling, control and simulation of a new tilt thruster underwater vehicle. The presented robot has four thrusters with variable thrust vectoring which are able to control six degrees of freedom. This underwater vehicle, by using two perpendicular servo motors for each thruster, provides independent and time variable orientation for each thruster. Change the thruster orientation as a function of time making it possible to move simultaneously to different directions and increase hovering ability. High maneuverability is an important advantage of this underwater vehicle. To demonstration of underwater robot ability to track the complex trajectory, in this paper a wide variety of paths are considered. In this paper, the robustness of the system under disturbances and parameters uncertainty are examined.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.083
GPT teacher head0.289
Teacher spread0.206 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

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