Effect of Wind on the Connectivity and Safety of Large Scale UAV Swarms
Bibliographic record
Abstract
Unmanned Aerial Vehicle (UAV) swarms are promising solutions to conduct different military and commercial missions. However, highly varying environmental factors such as wind make the UAVs unable to maintain the minimum safe distance between each other, and the UAVs at the edge of the swarm more vulnerable to connectivity loss from the swarm. The paper extensively studies the effect of wind on the connectivity and safety of a large scale swarm with one leader and multiple follower UAVs. We examined the relationship between different parameters including UAV speed, UAV mass, wind speed, drag force, and number of UAVs maintaining the desired safety and connectivity requirement in a swarm. Our analysis demonstrates that planning to fly the swarm at a speed closer (equal, if possible) to the mean value of the wind speed at the desired altitude increases the number of connected and safely flying UAVs. This choice of UAV speed, however, might not be the best solution when one considers the time constraint to reach the desired destination. And, when the UAVs are flying with a speed less than the wind speed, we get a smaller number of critical UAVs whereas, such a selection leads to a higher number of UAVs that do not meet the safety requirement of the swarm. To address this challenge, we propose to optimally and adaptively select the UAV speed so that the swarm reaches the target destination with the minimum/desired flight time while maintaining the connectivity and safety of most (all, if possible) of the UAVs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".