Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bearing-based hunting protocols commonly adopt a leaderless consensus method, which requests an entire state of the target for each agent and ignores the necessity of collision avoidance. We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols, where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance. The masters obtain the entire state of the target, whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process. However, the slaves’ protocols merely depend on the part state of the masters to reduce loads of data transmission. We also investigate the feasibility of receiving the bearing state from machine vision. The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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