Practical Implementation of Multi-UAV Flocking Path Planning
Bibliographic record
Abstract
The primary objective of this thesis is to implement a flocking path planning algorithm in the three-dimensional dense environment to take into account the aerodynamics downwash effect. A method to model the downwash force generated by the quadrotor unmanned aerial vehicle (UAV) and its effect on the neighboring UAVs is developed. A novel adaptive UAV model is proposed to optimize the path planning while minimizing the downwash impact by adjusting the UAV model zone size. The virtual zone around an UAV for collision-free path planning is modified from a standard spherical body to a proposed adaptive cylinder then to an adaptive cuboid. The cylinder height and the cuboid size vary based on the UAV circumstance and the predicted downwash impact. The flocking algorithm is modified for the adaptive model. The models are simulated and compared via Python and the Gazebo and Robot Operating System (ROS) simulation platform.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".