AUV controllability with control plane faults
Bibliographic record
Abstract
It may be important to be able to operate an autonomous underwater vehicle (AUV) when it has reduced control authority due to a control plane fault, such as a jammed or a missing control plane. Knowledge of how the vehicle behaves under these conditions will allow the mission planner to make critical decisions about the viability of the mission or about certain specific subtasks. Knowledge of vehicle behaviours under fault conditions can also facilitate the use of operational envelopes in restricted waters; i.e. a healthy AUV may be restricted in the magnitude of control plane deflections, so that it can maintain a safe trajectory even if a control plane fails. A systematic study was made involving simulations of the vehicle under fault conditions to identify the vehicle behaviours typical of such fault conditions. The simulation tool used is a linear model developed from a fully nonlinear model of the Canadian Self-Contained Off-theshelf Underwater Testbed (C-SCOUT), and the manoeuvres used were those most likely to be desired during normal operation: holding course, a controlled dive, and a turn in the horizontal plane. The fault condition simulations provide useful information, especially concerning safe operating envelopes for the CSCOUT for particular mission requirements. The information can also be used to enable the vehicle to perform selfdiagnosis procedures under some conditions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".