Aerial Coverage Planning for Areas Hidden from the View of a Moving Ground Vehicle
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of path planning for a UAV, deployed to provide sensor coverage ahead of a moving ground vehicle. The ground vehicle travels a fixed route through an uncertain environment and requires information about the area ahead. Given this route, the UAV planner calculates the regions to be covered and the time by which each must be covered, as an orienteering Problem with Time Windows (OPTW) and solves it using a Mixed Integer Linear Program (MILP). To improve scalability, we prove that the optimization can be partitioned into a set of smaller problems, each of which may be solved independently without loss of overall solution optimality. Finally, we demonstrate a method of limited loss partitioning, which can perform a trade-off between improved solution time and a bounded objective loss. All of our results are validated 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 | 0.000 | 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 | 0.001 | 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