Higher-Order Non-Autonomous Optimal Area Coverage Control
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Bibliographic record
Abstract
We present an area coverage control algorithm for multi-agent systems with order-k Voronoi partitions. The system is non-autonomous due to the uncontrolled dynamics of external agents operating in the environment. Area coverage control is an optimal resource allocation problem in which optimal agents’ configurations are stationary points of a coverage metric, consisting of centroidal Voronoi tessellations. We consider time-evolving environments with order-k Voronoi partitions, where Voronoi cells are defined by k-nearest generator rules. This applies to scenarios in which it is necessary and/or desirable to assign <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\gt 1$ </tex-math></inline-formula> agents to the trajectories of each cell. We prove that the proposed non-autonomous feedback control, with feed-forward dictated by the environment’s drift, asymptotically converges the agents to optimal centroidal order-k Voronoi configurations. Theoretical predictions are illustrated in simulation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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