Decentralized UAV Swarm Control: A Multi-Layered Architecture for Integrated Flight Mode Management and Dynamic Target Interception
Bibliographic record
Abstract
Uncrewed Aerial Vehicles (UAVs) are increasingly deployed across various domains due to their versatility in navigating three-dimensional spaces. The utilization of UAV swarms further enhances the efficiency of mission execution through collaborative operation and shared intelligence. This paper introduces a novel decentralized swarm control strategy for multi-UAV systems engaged in intercepting multiple dynamic targets. The proposed control framework leverages the advantages of both learning-based intelligent algorithms and rule-based control methods, facilitating complex task control in unknown environments while enabling adaptive and resilient coordination among UAV swarms. Moreover, dual flight modes are introduced to enhance mission robustness and fault tolerance, allowing UAVs to autonomously return to base in case of emergencies or upon task completion. Comprehensive simulation scenarios are designed to validate the effectiveness and scalability of the proposed control system under various conditions. Additionally, a feasibility analysis is conducted to guarantee real-world UAV implementation. The results demonstrate significant improvements in tracking performance, scheduling efficiency, and overall success rates compared to traditional methods. This research contributes to the advancement of autonomous UAV swarm coordination and specific applications in complex environments.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".