Unmanned Aerial Vehicle Landing on Maritime Vessels using Signal Prediction of the Ship Motion
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are becoming more prevalent in maritime operations. For safe operation, one of the key challenges of using UAVs at sea is the relative motion that exists between the UAV and ship. For perpetual maritime operations, UAV systems need to be able to land safely on ocean vessels. Determining a `quiescent period', where the roll and pitch angles of the ship are below a danger threshold, is a challenging problem for UAV systems. In general, current strategies rely on reactive systems and often use sensors on board the maritime vessel. The scope of the current paper is a proof-of-concept methodology which uses a signal prediction algorithm to facilitate safer autonomous UAV-ship landings. This study uses laser ranging and detecting devices (LIDAR) in conjunction with a signal prediction algorithm (SPA) to forecast when the ship motion is within safe landing limits. ShipMo3D was used to generate twelve trial cases for UAV-ship landings on a 33 m ship. The results show that with the use of the SPA, the number of UAV landing attempts was decreased by an average of 2 attempts, per test case, when compared to a system that did not use an SPA. Moreover, the results indicate that with revised tuning of the SPA, the likelihood of a safe landing can be further improved.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it