A cooperative UAV/UGV platform for wildfire detection and fighting
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have received much attention in the research and development community due to their strong potential in certain high-risk missions. In applications that involve multiple vehicles, the inter-vehicle communication and cooperation becomes a critical challenge to a successful mission. An effective co-operative control framework is required to co-ordinate the system-level decision making process and information flow among the multiple agents such that the collective mission is optimally achieved. In this paper, a cooperative control framework for a hierarchical UAV/UGV platform is proposed. A top-level mobile mission controller provides effective mission planning and system-level decision making such that mission completion time and resource expenditure are optimized. The mobile mission controller can monitor the dynamic environment with its own sensing capabilities and coordinate UAVs/UGVs in their actions. This paper discusses the potential application of the proposed hierarchical vehicle platform to high-risk missions, specifically in the context of wildfire fighting. The task generation and allocation problems and proposed approaches are presented under the given control framework.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".