SOScheduler: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAV Collaborative Scheduling
Bibliographic record
Abstract
Multi-UAV systems have shown immense potential in handling complex tasks in large-scale, dynamic, and cold-start (i.e., limited prior knowledge) scenarios, such as wildfire suppression. Due to the dynamic and stochastic environmental conditions, the scheduling for sensing tasks (i.e., fire monitoring) and operation tasks (i.e., fire suppression) should be executed concurrently to enable real-time information collection and timely intervention of the environment. However, the planning inclinations of sensing and operation tasks are typically inconsistent and evolve over time, complicating the task of identifying the optimal strategy for each UAV. To solve this problem, this paper proposes SOScheduler, a collaborative multi-UAV scheduling framework for integrated sensing and operation in large-scale and dynamic wildfire environments. We introduce a spatio-temporal confidence-aware assessment model to dynamically and directly pinpoint locations that can optimally enhance the understanding of environmental dynamics and operational effectiveness, as well as a priority graph-instructed scalable scheduler to coordinate multi-UAV in an efficient manner. Experiments on real multi-UAV testbeds and large-scale physical feature-based simulations show that our SOScheduler reduces the fire expansion ratio by 59% and enhances the fire coverage ratio by 190% compared to state-of-the-art (SOTA) solutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".