Multi-UAV Cooperative Hunting in Cluttered Environments Considering Downwash Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a novel solution to the three-dimensional (3D) cooperative hunting of multiple drones that deals with surrounding a target simultaneously while navigating around obstacles in the cluttered dynamic 3D environment. Meanwhile, drones avoid the airflow downwash force created by the spinning propellers on unmanned aerial vehicles (UAVs) and their effect on the other UAVs. This solution consists of a 3D Simultaneous Encirclement strategy, the cooperative hunting objective with a novel revised particle swarm optimization (PSO*) path planning algorithm, a flocking theory-inspired obstacle avoidance algorithm, and a cascade PI controller. Simulation results with varying conditions were carried out to validate the effectiveness of the proposed solution by successfully taking care of the downwash effects, and having multiple hunter UAVs hunt and encircle a moving or stationary target in a dynamic or static obstacle-rich cluttered environment.
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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 | 0.001 | 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 | 0.001 |
| 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 it