Drone's trajectory optimization for searching a disaster collapsed area using data mining techniques
Bibliographic record
Abstract
Drones have unique characteristics such as mobility, flexibility, and role adaptability. Hence, they are useful tools during urban search and rescue (USAR) operations in areas where many buildings have suffered structural collapse leading to difficulties in reaching locations where people may be trapped. However, drones are limited by the life of the batteries allowing their flight. Clearly, reducing flight time to useful destinations can help to optimize the use of drones with limited battery life. Therefore, this study considers trajectory optimization as a critical research problem for USAR applications. This paper explores optimal trajectory paths for USAR operations for a single drone by selecting nodes within a disaster area from the start point to the destination point for the drone's flight. The following analysis applies density-based spatial clustering of applications with noise (DBSCAN) data clustering algorithm to the dataset obtained from the scanning from the top of the disaster collapse area. The goal is to find the optimized shortest paths for USAR drone operations in order to conserve drone flight time. The computational results are compared to physical experimental results.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".