Development of a Plug-and-Play Infrared Landing System for Multirotor Unmanned Aerial Vehicles
Bibliographic record
Abstract
Precise landing of multirotor unmanned aerial vehicles (UAVs) in confined, GPS-denied and vision-compromised environments presents a challenge to common autopilot systems. In this work we outline an autonomous infrared (IR) landing system using a ground-based IR radiator, UAV-mounted IR camera, and image processing computer. Previous work has focused on UAV-mounted IR sources for UAV localization, or systems using multiple distributed ground-based IR sources to estimate UAV pose. We experimented with the use of a single ground-based IR radiator to determine the UAV's relative location in three-dimensional space. The outcome of our research significantly simplifies the landing zone setup by requiring only a single IR source, and increases operational flexibility, as the vision-based system adapts to changes in landing zone position. The usefulness of our system is especially demonstrated in vision-compromised applications such as nighttime operations, or in smoky environments observed during forest fires. We also evaluated a high-power IR radiator for future research in the field of outdoor autonomous point-to-point navigation between IR sources where GPS is unavailable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".