Distributed coordination of multi-agent systems for coverage problem in presence of obstacles
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents new algorithms for distributed deployment of a network of multi-agent systems to minimize prescribed cost function. It is assumed that the so-called “operation cost” of distinct agents can be different. The problem is investigated for both cases of an obstacle-free environment and a fixed-obstacle environment. For the former case, the center multiplicatively weighted Voronoi (CMWV) configuration is introduced, and it is shown to be the optimal configuration. A distributed coverage control is also provided which guarantees that the configuration of the agents converges to this optimal configuration. For the case of a fixed-obstacle environment, the visibility-aware multiplicatively weighted Voronoi (VMW-Voronoi) diagram is introduced and a motion coordination strategy is presented to achieve the desired objective. Simulations demonstrate the effectiveness of the proposed algorithms in both cases.
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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 | 0.000 |
| 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