Voronoi-Based Multi-UAV Deployment for Infrastructure Inspection After Disasters
Bibliographic record
Abstract
Following natural disasters, the rapid inspection of critical infrastructure is essential for ensuring public safety and guiding emergency response. To evaluate damage and functionality, it is important to quickly assess key sites, such as hospitals, power plants, bridges, and emergency shelters. In this study, we propose an energyconstrained trajectory planning framework for a swarm of Unmanned Aerial Vehicles (UAVs) tasked with inspecting predefined infrastructure locations across a disaster-affected area. The approach begins by partitioning the affected surface using a Voronoi diagram and assigning each UAV a specific region to operate within. Within each region, the trajectory optimization problem is formulated as a mixedinteger linear programming problem (MILP) with an energy-based objective function. A genetic algorithm (GA) is applied to efficiently solve the optimization problem, and its performance is compared to the Nearest Neighbor Algorithm (NNA). The experimental results demonstrate that the GA achieves better energy consumption, whereas the NNA provides faster execution times.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".