An Algorithm for Flock Formation Control using Distributed Consensus
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a formation control algorithm based on a flocking approach to coordinate a team of quadro-tors. A flock formation control algorithm aims to respect for Reynolds rules (cohesion, alignment and separation) whereas maintaining a formation shape of Unmanned Aerial Vehicles (UAVs) team defined a priori. In the proposed algorithm, the navigation approach for flocking algorithm presented by Olfati-Saber is reformulated for keeping a predefined formation shape, propagating the desired formation in the network using a distributed consensus algorithm, starting from a virtual leader. The proposal is validated using a Model-in-the-Loop (MiL) simulation composed by the linear models of quadrotors and the position, altitude and attitude proportional-derivative controllers. The results obtained were presented and analysed based on graphic resources and metrics for formation control and flocking behaviour algorithms.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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