Non-autonomous State-Feedback to Stabilize the Error Dynamics in Time-Varying Area Coverage Control Problems
Bibliographic record
Abstract
We propose a state-feedback control scheme for area coverage problems using a group of agents operating in a time varying environment. The coverage metric defining the optimal control problem encodes a time-varying risk density that models an evolving environment in which the agents operate. The evolution of the environment is caused by the presence of mobile external objects (targets). Maximum coverage can be accomplished by deploying more (less) agents to the part of the area marked with a high (low) risk density, resulting into non-uniform agents' distributions that adapt to the environment. The workspace is partitioned according to a Voronoi tessellation with respect to a distance that quantifies robots' sensing performances. For every initial configuration of the group of agents in the workspace, the proposed non-autonomous state-feedback controller asymptotically drives the agents to time varying centroids of the Voronoi tessellation, therefore positioning them in the optimal configuration with respect to the performance measured by the coverage metric. The proposed control scheme is illustrated by simulations.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".