U-SMART: unified swarm management and resource tracking framework for unoccupied aerial vehicles
Bibliographic record
Abstract
Unoccupied aerial vehicle (UAV) swarms have the ability to exhibit improved capabilities and performance when compared to individual UAVs. However, their target operation environment is fraught with disruptions, including communication limitations, sensor failures, and dynamic environmental conditions, which can significantly impact swarm performance and robustness. To address these challenges, the proposed unified swarm management and resource tracking (U-SMART) framework focuses on enabling resiliency within UAV swarms. Resiliency refers to the swarm's ability to adapt, recover, and maintain functionality in the face of disruptions. The framework integrates features such as agent well-being tracking, collision and obstacle avoidance, energy management, and task control to enhance the swarm's ability to withstand disruptions and continue operating effectively to provide a comprehensive solution for unified swarm management. The modular design allows flexible configuration, upgrades, and the addition of new components. This facilitates easy adaptation to specific swarm requirements and evolving operational needs. Using frameworks like U-SMART, swarm operators can efficiently manage and control UAV swarms, mitigate disruptions, and maintain high situational awareness in challenging environments. Performance is validated for the integrated modules to test feasibility for different experiment scenarios. For each module and feasibility test, thresholds were set to indicate acceptable performance in the presence of disruptions, and results for the swarm running on the proposed framework showed the acceptable performance of agents validated using explicitly designed metrics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".