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Record W2089587434 · doi:10.1109/oceans.2008.5151986

Unmanned surface vehicles for undergraduate engineering education

2008· article· en· W2089587434 on OpenAlexaboutno aff
Joseph Holler, Stephen Longfield, Katherine Murphy, Andrea Striz, Brian Bingham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)RoboticsField (mathematics)Engineering educationProject-based learningEngineering managementAeronauticsUnmanned surface vehicleRobotEngineeringScience and engineeringComputer scienceSystems engineeringArtificial intelligenceEngineering ethicsMechanical engineeringMathematics educationMarine engineeringPsychology

Abstract

fetched live from OpenAlex

For the past two years undergraduate engineering students from Olin College of Engineering have worked to develop unmanned surface vehicles as low cost educational platforms for scientific research. As an undergraduate project, the work on the USV involved the student team in a hands-on experience that included in-house design, fabrication, and field operations. Working dominantly out of the Olin Field Robotics Laboratory the first fully operational vehicle completed an unmanned field test in the spring of 2008. Previously in 2007, two students from this project team presented the academic benefits of this self-directed work for engineering and science education at the MTS/IEEE Ocean's Conference in Vancouver. Here we present the data collected from the first autonomous trials carried out by the vehicle as well as the continued work since the Oceans 2007 publication. This work includes the completion of the mechanical design, creation and verification of a mathematical model of the vehicle, and implementation of autonomous control. Lastly, this paper continues to testify to the core benefits of an unmanned vehicle as an undergraduate engineering project, leading not only a to viable vehicle platform but also an extremely valuable learning experience for the team involved.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.398

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 science0.0000.000
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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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
GenreEmpirical

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

Citations6
Published2008
Admission routes1
Has abstractyes

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