A Cooperative Heterogeneous System Design of Unmanned Aerial and Surface Vehicles
Bibliographic record
Abstract
Unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) have been widely applied in maritime operations. However, challenges persist in the formation control and joint operations of such heterogeneous unmanned systems. This article presents a novel cooperative system design of USVs and UAVs aimed at synchronized cooperative tasks and precise joint operations. To enhance the precision of joint operations, an advanced vision guidance system is introduced to address the limitations of GPS accuracy. Leveraging the topological characteristics of the proposed landmark, efficient methods are developed for dynamic binarization, target selection, and pose solving. A path coordination method is proposed to ensure continuous transitions from cooperative tasks to joint operations. Controllers for USVs and UAVs are designed using the backstepping technique to regulate the position and speed of each vehicle along the coordinated paths. The effectiveness and advantages of these proposed methods are validated through outdoor experiments. The supplementary video of these experiments is available athttps://www.bilibili.com/video/BV1vJ4m1P75G.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".