Cooperative control of multiple UAVs for forest fire monitoring and detection
Bibliographic record
Abstract
This paper proposes to use multiple cooperative unmanned aerial vehicles (UAVs) for forest monitoring, fire detection and tracking of its propagation. The proposed algorithm solves the problems of forest fire by including three stages of search, confirmation and observation. During the search stage, the UAVs team moves in a certain formation shape in a leader-follower approach, a distributed sliding mode formation control is designed to keep the desired formation shape during this stage. Once a fire is detected, all sensory data will be sent to the ground station. A new reference trajectory is calculated according to the fire spread model for generating an elliptic fire perimeter. The team begins following the new fire trajectory, afterward the leader will send reconfiguration commands to followers. Therefore, a distributed reconfigurable controller is designed based on sliding mode control (SMC) which converts the formation problem from 2-D Cartesian frame of reference to the Polar frame of reference. This algorithm is used for evenly distributing and tracking UAVs team for elliptical fire perimeter. The effectiveness of the proposed algorithm is demonstrated using a six degree-of-freedom (DOF) quadrotor dynamic model and a simplified fire front model.
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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 | 0.000 |
| 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 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".