Formation Control of Multi-agent Systems via Voronoi Tessellation and Kullback-Leibler Divergence
Bibliographic record
Abstract
We present an algorithm to control the spatial distribution of kinematic multi-agent systems in two-dimensional workspace. Leveraging on the coverage control framework, we formulate the problem as a multi-objective optimization with a performance index composed of the area coverage metric and of the Kullback-Leibler (KL) divergence. The KL term drives the statistical spatial distribution of the agents to a desired, user-defined density in the workspace, whereas the coverage term drives the agents to a centroidal Voronoi configuration. The two terms are connected by setting the target distribution to be also the risk density in the area coverage term. The risk density for the coverage metric weights points in the area based on their relative importance. We prove that the proposed control law minimizes the multi-objective metric by driving the agents to a generalized centroidal Voronoi configuration along the trajectories generated by the gradient of the performance index, while minimizing the distance between the moments of the agents’ distribution and of the target distribution. The proposed control allows to use the target distribution to drive the system’s formation. Theoretical predictions are illustrated in simulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".