Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle
Bibliographic record
Abstract
We have developed an adaptive sampling algorithm for an Explorer autonomous underwater vehicle (AUV) to conduct in-situ analysis of acoustic measurements to perform autonomous oil plume detection and tracking. The methodology of the tracking phase involves ongoing analysis of the detected plume, assessing target validity and proximity for AUV decision-making for plume mapping. We previously introduced the bumblebee flight path, a new biomimetic search pattern designed to maximize the spatial coverage in the oil plume detection phase. This paper focuses on a new tracking strategy as the key adaptive stage in our plume delineation. For initial development we used a 360-degree scanning sonar sensor model. Simulations were done with different plume models to assess the performance of the developed adaptive sampling algorithm. A convergence study demonstrated that the algorithm could successfully track the boundary of a non-regular shaped/patchy oil plume at up to a 0.05Hz sampling frequency. A sensitivity study identified the correlations between plume feature complexity and the anticipated range of acoustic measurement update delays. The decision-making architecture consists of three separate components which implements either proximity or boundary following control and contributes to the final decision on the next desired heading of the vehicle. A weight ratio, that determined the relative allocation of each component, was varied to study its impact on the tracking performance of the AUV. The novelty of our approach is in addressing the discontinuous and patchy nature of realistic oil plumes. Our sampling algorithm and its performance in simulations is a significant step beyond the practical limitations of existing gradient-following methods because it accounts for the oil patches and droplets which gradient-following algorithms do not.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".