Aerial-Marine Cross-Domain Uncrewed Systems: An Overview of Cyberphysical Coordination Frameworks for Marine Applications
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.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".