Cloud-Based Self-Triggered Cooperative Path Following of Underactuated USVs With Multimodel Extended State Observers
Bibliographic record
Abstract
This article addresses the cooperative path following problem for multiple underactuated unmanned surface vehicles (USVs) in the presence of limited cloud communication and various sailing conditions. The USVs are only able to communicate with their neighbors intermittently through a cloud server. At the kinematic level, a self-triggered cooperative guidance law is proposed to allow all USVs to communicate with the cloud only when necessary rather than at a fixed period. Each USV estimates the states of its neighbors in real-time and autonomously determines the next communication time, such that it can stay disconnected from the cloud for longer periods of time and no listening is needed. At the dynamic level, a model-free kinetic controller is proposed based on multimodel extended state observers (ESOs). Multiple ESOs with different nominal control input parameter values are synthesized and a monitoring mechanism is designed to select the most suitable one at one time instant. Simulation results demonstrate the effectiveness of the proposed cloud-based self-triggered cooperative path following for multiple USVs with multimodel ESOs.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".