Intelligent Path Planning and Following for UAVs in Forest Surveillance and Fire Fighting Missions
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
As a flexible, efficient, and powerful platform for a variety of practical applications, the unmanned aerial vehicle (UAV) has attracted increasing attention in the field of forest fire monitoring, detection, and tracking in recent years. In forest surveillance and fire fighting missions, threats may occur when a UAV is assigned to fly over a forest area, as statical obstacles like hills may stand between the base and the fire spot, and dynamic obstacles like birds may appear during the flight. To deal with these challenges, a path planning algorithm that can learn the terrain environment and generate the motion policy to plan an optimal path is developed. A hierarchical structure is adopted for path planning to achieve the optimal result in a global manner, as well as avoid the curse of dimensionality. Moreover, an intelligent path following algorithm that can perceive and avoid dynamic obstacles is also developed for the UAV to accomplish the task safely. Numerical simulations are conducted to validate the proposed algorithms.
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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 | 0.000 |
| 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 it