Multi-UAV based Autonomous Wilderness Search and Rescue using Target Iso-Probability Curves
Bibliographic record
Abstract
The application of unmanned aerial vehicles (UAVs) to searches of lost persons in the wilderness can significantly contribute to the success of the missions. Maximizing the effectiveness of an autonomous multi-UAV search team, however, requires optimal task allocation between the team members, as well as the planning of the individual flight trajectories. This paper addresses this constrained resource-allocation optimization problem via the use of iso-probability curves that represent probabilistic target-location information in a search region growing with time. The optimization metric used is the allocation of the search effort proportional to the target location likelihood. The proposed method also avoids redundancy in coverage while planning the UAV trajectories. Numerous simulated search experiments, two of which are detail herein, were carried out to demonstrate our method's effectiveness in wilderness search and rescue (WiSAR) planning using a multi-UAV team. Extensive comparative studies were also conducted to validate the tangible superiority of our proposed method when compared to existing WiSAR techniques in the literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".