Target Search on Road Networks With Range-Constrained UAVs and Ground-Based Mobile Recharging Vehicles
Bibliographic record
Abstract
We study a range-constrained variant of the multi-UAV target search problem where commercially available UAVs are used for target search in tandem with ground-based mobile recharging vehicles (MRVs) that can travel, via the road network, to meet up with and recharge a UAV. We propose a pipeline for representing the problem on real-world road networks, starting with a map of the road network and yielding a final routing graph that permits UAVs to recharge via rendezvous with MRVs. The problem is then solved using mixed-integer linear programming (MILP) and constraint programming (CP). We conduct a comprehensive simulation of our methods using real-world road network data from Scotland. The assessment investigates accumulated search reward compared to ideal and worst-case scenarios and briefly explores the impact of UAV speeds. Our empirical results indicate that CP is able to provide better solutions than MILP, overall, and that the use of a fleet of MRVs can improve the accumulated reward of the UAV fleet, supporting their inclusion for surveillance tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".