Coordinated 3D path following control for a team of UAVs with reference velocity recovery
Bibliographic record
Abstract
In this paper we develop new type of control law to steer a group of multiple unmanned air vehicles (UAVs) along given spacial paths (path-following), while ensuring that they reach and maintain a desired formation pattern. The methodology utilized to derive the cooperative path following controller unfolds in two basic steps. First, a path-following control law is derived based on the use of the 3D Serret-Frenet formulation to represent the UAV kinematics in terms of path parameters, which allows for convenient definition of cross, along and vertical-track error, to steer each vehicle to its assigned path regardless of the speed profile adopted. In second step, the speeds of the UAVs are adjusted so as to synchronize, the position of the corresponding UAVs. Unlike previews research work designs that assume availability of the reference velocity to each UAVs, we consider the situation where this information is only available to a leader of this formation. The control scheme relies on an adaptive design to estimate the reference velocity which the other UAVs need to reconstruct to recover the desired formation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".