Unmanned surface vehicles for undergraduate engineering education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".