Aerial-Marine Cross-Domain Uncrewed Systems: An Overview of Cyberphysical Coordination Frameworks for Marine Applications
Why this work is in the frame
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Bibliographic record
Abstract
With the swift progress of ocean engineering and marine economy, marine missions are becoming more complex. This upsurge in complexity is leading to the integration of fleets of uncrewed aerial vehicles (UAVs) and fleets of uncrewed surface vessels (USVs) into an aerial–marine cross-domain uncrewed system (AMCDUS). Such an integration has become indispensable for fulfilling increasingly challenging marine missions. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">To build a foundation of the essential cooperation theories, techniques, and applications of the AMCDUS, we propose a hierarchical cyberphysical coordination framework that fuses the physically coordinated motions with the information that flows among the terminal, the network, and the cloud (see “Summary” section). The top level in the physical space is designed to produce coordinated planning paths for the middle level of multi-UAV–USV coordination. The middle level accordingly conducts mission-based cooperation commands and yields velocity and heading references for the low level of individual vehicle capabilities. The top level in cyberspace is implemented in cloud servers, which calculates the cross-domain intelligent control law to accommodate critical situations. Then, the middle-level network servers make cooperative commands for heterogeneous uncrewed systems, and the low-level terminal servers conduct both environmental perception and target recognition with the assistance of various infrastructures and sensors. Extensive coordinated cross-domain navigation and landing experiments of AMCDUSs are conducted to validate the effectiveness of the proposed framework. Emerging challenges are discussed to motivate future directions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it