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Record W2095668486 · doi:10.1109/icca.2010.5524083

Multi-thread technology based autonomous underwater vehicle

2010· article· en· W2095668486 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSonarThread (computing)Computer scienceObstacle avoidanceUnderwaterReal-time computingRemotely operated underwater vehicleObstacleMobile robotControl engineeringSimulationRobotEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

An autonomous underwater vehicle (AUV) is implemented with multi-thread technology under QNX Neutrino Real time Operating System. The hybrid system and autonomous control principle of AUV are introduced. The event generator thread and event analysis and decision making thread are two key threads in the autonomous control system to handle discrete events, which are described in detail. Also the histogram in motion mapping algorithm with growth rate operator is adopted to process the sonar data and to express the uncertainty of sonar data. The obstacle avoidance algorithm based on sonar data is designed to enable vehicle to avoid obstacle immediately, effectively and safely. Finally we verify this autonomous control system with simulation study and experiment in lake. The simulation and experiment results show that the proposed AUV autonomous control system can fulfill the missions effectively.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.943
Threshold uncertainty score0.816

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.257
Teacher spread0.238 · 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

Quick stats

Citations3
Published2010
Admission routes1
Has abstractyes

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