Optimal flight path planning for UAVs in 3-D threat environment
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
Nowadays, the environments surrounding modern battlefield are becoming increasingly complicated, since the threats are not only from the ground but also from the sky. UAV with reconnaissance mission will take more risk when flying along an improper planned path, so path planning of UAV in complex 3-D environments is very significant and challenging. Aimed at the problem, this paper proposes a novel optimal path planning method for UAV based on the flight space partitioning, Dijkstra algorithm and potential field theory. Specifically, under the cases that the locations of threats are assumed to be known and the whole flight space is partitioned into a number of cells and each cell has a safest node. Then, a 3-D network is formed by connecting the nodes of adjacent cells and a shortest suboptimal path is marked on the network with Dijkstra algorithm. Finally, the optimal path is obtained with artificial potential field method. To verify the proposed algorithm, simulation results in two cases are shown.
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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 | 0.000 | 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 | 0.001 | 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