UAV swarm communication and control architectures: a review
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have significantly disrupted the aviation industry. As technology and policy continue to develop, this disruption is only going to increase in magnitude. A specific technology poised to escalate this disruption is UAV swarm. UAV swarm has the potential to distribute tasks and coordinate operation of many UAVs with little to no operator intervention. This paper surveys literature regarding UAV swarm and proposes a swarm architecture that will allow for higher levels of swarm autonomy and reliability by utilizing cellular mobile wireless communication infrastructure. This paper chronicles initial testbed development to meet this proposed architecture. Focused development of UAV swarms with UAV-to-UAV communication autonomous coordination ability is central to advancing the utility of UAV swarms. The use of cellular mobile framework alleviates many limiting factors that hinder the utility of UAVs including range of communication, networking challenges, and size-weight-and-power considerations. In addition, cellular networks leverage a robust and reliable infrastructure for machine to machine communication proposed by 5G systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".