Semi-Autonomous Control of Drones/UAVs for Wilderness Search and Rescue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wilderness search and rescue (WiSAR) has been one of the most significant robotic applications in the past decade. In order to succeed in these life-saving operations, the deployment of drones or unmanned aerial vehicles (UAVs) has become an inevitable trend. This paper presents the development of a low-cost solution for semi-autonomous control of drones/UAVs in WiSAR applications. ArduPilot based flight controller was implemented to enable autonomous trajectory following of the drones/UAVs. A high resolution action camera attached to the drone/UAV was used to take video footage during the flight, which was related to the GPS location through the time stamp. The recorded video footage was manually transferred to a laptop for potential target detection using OpenCV and YOLOv3. The system design is reported in detail, and experiments were conducted to verify the effectiveness of the developed system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it