Zero visibility autonomous landing of quadrotors on underway ships in a sea state
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) are a valuable resource and have many applications in marine environments. The UAV's capabilities can be further augmented through increased autonomy. One potential area for more autonomy is landing on the stern of underway ships. This requires good position estimates that are independent of GPS and in many harsh environments, vision. We demonstrate a position estimation and tracking technique that uses peer-to-peer acoustic range measurements coupled with the relative inertial measurement unit estimates from the ship and UAV to create a robust state estimation. A Stewart-Gough platform is used to emulate the motions of a ship stern, where the UAV would land, in different sea-states and a state-of-the-art motion capture system provides the ground-truth positioning for the UAV and the Stewart-Gough platform. We demonstrate a pose estimate with a mean-squared error (MSE) of 9.41 cm (23.8% of landing area length) and a “ready-to-land” position tracker with an MSE of 12.05 cm (30.4% of landing area length). The proposed extended Kalman Filter used to fuse the measurements is more than adequate. The zero visibility autonomous landing algorithm works well and the next stage will be validation at-sea.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".