Linear model predictive control for vision-based UAV pursuit
Bibliographic record
Abstract
This article develops and implements a vision-based unmanned aerial vehicle (UAV)-to-UAV pursuit system using a commercial off-the-shelf Parrot AR.Drone 2.0 quadrotor. This technology is intended as a countermeasure to rogue drones carrying out activities such as flying in restricted airspace, performing unauthorized aerial videography, transporting contraband and other criminal activities, or being used as improvised weapons. The proposed approach offers benefits over other current solutions, such as wide-area radio-frequency jamming that interferes with regular communication devices or high-energy military laser systems that are expensive and time consuming to set up. A linear dynamics model of the AR.Drone 2.0 vehicle stabilized by its onboard feedback control system is derived, and its parameters are experimentally identified. A linear model predictive control is developed to track specified flight trajectories, then implemented and validated in hardware flight tests. Detection and ranging of the target UAV from the pursuer UAV’s onboard monocular camera are performed using the YOLO v2 convolutional neural network algorithm. The combined control and vision design is implemented in hardware and tested quantitatively in flight experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".