NetherDrone: a tethered and ducted propulsion multirotor drone for complex underground mining stope inspections
Bibliographic record
Abstract
Underground stope mapping is crucial to evaluate the quantity of blasted rock and the site integrity. In recent years, lidar-equipped drones have been used to map stopes with higher precision and without blind spots. However, they have limitations, such as large size, challenging lidar positioning on the drone, limited flight time for detailed visual inspections, and unreliable communication underground. This paper discusses the development of a compact tethered drone called the NetherDrone, specifically designed for stope inspections. The NetherDrone uses custom ducted propulsion to increase thrust efficiency by 50%. It reduces the propellers’ diameter and overall frame while maintaining an adequate lifting capability with low power consumption. The drone features an onboard 120 m tether spool for communication and power transmission, as well as a rotating arm to deploy the cable and reduce yaw moments from the tether tension. Flights in a real stope demonstrated that the drone could effectively move at least 50 m deep into a complex stope, complete a detailed lidar scan, visually scan one face of the stope in close proximity during 20 min, travel a total distance of 270 m, and maintain communications with an operator at all times through the tether.
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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) | 0.002 | 0.001 |
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".