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Record W2120461516 · doi:10.1177/0278364908098560

Sensor-based Behavior Control for an Autonomous Underwater Vehicle

2009· article· en· W2120461516 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe International Journal of Robotics Research · 2009
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUnderwaterComputer visionComputer scienceArtificial intelligenceOrientation (vector space)RobotSet (abstract data type)

Abstract

fetched live from OpenAlex

In this paper, we evaluate a set of core functions that allow an underwater robot to perform surveillance under operator control. Specifically, we are interested in behaviors that facilitate the monitoring of organisms on a coral reef, and we present behaviors and interaction modes for a small underwater robot. In particular, we address some challenging issues arising from the underwater environment: visual processing, interactive communication with an underwater crew and, finally, orientation and motion of the vehicle through a hovering mode. The visual processing consists of target tracking using various techniques (color segmentation, color histogram and mean shift). Communication underwater is achieved through printed cards with robustly identifiable visual markers on them. Finally, the hovering gait developed for this vehicle relies on the planned motion of six flippers to generate the appropriate forces.

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.

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 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 score0.593
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.104
GPT teacher head0.372
Teacher spread0.268 · 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