Asymmetric Rendezvous Search at Sea
Bibliographic record
Abstract
In this paper we address the rendezvous problem between an autonomous underwater vehicle (AUV) and a passively floating drifter on the sea surface. The AUV's mission is to keep an estimate of the floating drifter's position while exploring the underwater environment and periodically attempting to rendezvous with it. We are interested in the case where the AUV loses track of the drifter, predicts its location and searches for it in the vicinity of the predicted location. We parameterize this search problem with respect to both the uncertainty in the drifter's position estimate and the ratio between the drifter and the AUV speeds. We examine two search strategies for the AUV, an inward spiral and an outward spiral. We derive conditions under which these patterns are guaranteed to find a drifter, and we empirically analyze them with respect to different parameters in simulation. In addition, we present results from field trials in which an AUV successfully found a drifter after periods of communication loss during which the robot was exploring.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".