Swarm of drones for surveillance monitoring of a grounded target: an event-triggered approach
Bibliographic record
Abstract
In this paper, we present a novel formation control approach in the framework of event-triggered (ET) control to provide a solution to the surveillance problem. To do this, we identify two main challenges which are the switching topology of the drones and the limited bandwidth of the communication network, which are also valid in formation applications. To provide a solution to switching topologies, we propose a networked continuous controller that is robust in the presence of connection switching between drones and the target. Then, we propose a networked controller with ET communication in some aperiodic instants which reduces the required bandwidth and load within the communication network. We guarantee the stability of the developed ET controller and prove that the Zeno behavior cannot occur. To validate the method, we present realistic 3D simulation results conducted in the Simulink environment of Matlab ® for different scenarios. The results of the study show the effectiveness of the proposed controller, especially for limited bandwidth channels as the ETC scheme has decreased the load within the communication network while resulting in a robust and efficient formation performance. We also considered moving target scenarios with missing possibilities to validate the robustness of the proposed method.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".