Adaptive Heading Controller on an Underwater Glider for Underwater Iceberg Profiling
Bibliographic record
Abstract
Abstract A Slocum-class underwater glider has been modified for autonomous mapping of the underside of icebergs. A scanning sonar has been integrated inside the extended nose-section of the vehicle. The sonar is oriented to scan a sector to forward-side of the vehicle. A control algorithm using returns from the sector scanning sonar has been implemented in order to adapt the path of the vehicle around an iceberg. With the sonar implemented together with the adaptive heading controller, the Slocum glider is programmed to circumnavigate the target iceberg at a desired standoff distance. In this paper, the design of the adaptive control algorithm will be presented. Initially, the control algorithm is validated in a simulation environment that models the iceberg-profiling mission for a moving iceberg with the modified Slocum underwater glider. In July 2015, the Slocum glider was deployed to map an iceberg in Conception Bay, Newfoundland, with the proposed adaptive controller integrated. The detailed planning for this field trial together with results will be presented. The results show that using the Slocum-class underwater glider for underwater iceberg profiling has the potential of reducing the operational cost, while improving the quality of the data obtained on icebergs. The operation of underwater glider only requires minimal number of operational personnel and equipment. The acoustic noise is much lower than for a larger support vessel, and the glider can stay closer to the iceberg resulting in improved quality of the sonar measurements. More importantly, environmental data around the iceberg, such as salinity, water temperature and potentially water current profiles, are also measured during the mission that is necessary for scientists in understanding iceberg dynamics leading to an improved iceberg drift prediction model.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".