Learning for Path Planning and Coverage Mapping in UAV-Assisted Emergency Communications
Bibliographic record
Abstract
We consider a setting in which a rotary-wing unmanned aerial vehicle (UAV) acts as an aerial base station to provide emergency communication service to an area of unknown and inhomogeneous user distribution. The UAV has communication with a ground node deployed to the area, which acts as a charging station. We are interested in two important problems in this setting, namely the path planning and coverage mapping problems. In the path planning problem, the UAV must plan its path starting and ending at the charging station, visiting a series of waypoints over which it hovers to provide coverage to surrounding users. On the other hand, the coverage mapping problem focuses on learning the distribution of user coverage over the area. We highlight the importance of learning this distribution to collect valuable data in an emergency situation. We then propose an online algorithm that simultaneously solves the path planning and coverage mapping problems using a deep learning model. We highlight the interplay and conflicting goals of path planning and coverage mapping, but show through Monte Carlo simulation that, under the correct parameters, the algorithm is able to achieve success on both problems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".