Maneuvering Planning for UAVs in Forest Surveillance and Fire Detection Missions with Kinematic Uncertainties
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have brought increasing spotlights to the area of forest fire detection, monitoring, and tracking in latest years because of their high flexibility, efficiency, lower cost, and less risk without the need for pilot onboard. Commonly, heavier and more complex tasks can be accomplished by employing multiple UAVs in a formation instead of a single UAV. However, one potential trouble is that the power could constantly exhaust or faults could happen on UAVs in the formation in the field, therefore the weakened UAVs must be supplanted by new vehicles from the UAV base. Thus, the problem is to find an optimal way to navigate the recent UAVs to join the team and to maintain the formation for the remaining task. Additional difficulties arise when uncertainties of motions are encountered over the flight. To overcome these challenges, an uncertainty-embedded maneuvering planning strategy based on the Star-Minimax algorithm is developed. Simulation results prove that a newly assigned UAV can be effectively navigated to join the UAV team, and the formation can be sustained during the flight even in the presence of kinematic uncertainties. Flight experiments, operated in the NAV Lab at Concordia University, further validate the functional performance of the proposed maneuvering planning strategy in real-time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".