Experimental Demonstration of the Lifting Capability of a Towed Payload Using Multiple Fixed-wing UAVs
Bibliographic record
Abstract
This paper presents an experimental demonstration of the Giant Rotor System (GRS), a heavy-lifting aircraft concept based on the Electric Power Reconfigurable Rotor (EPR²) concept and subsequent studies. The GRS is the first system to successfully demonstrate, under real outdoor flight conditions, the lifting of a payload with two off-the-shelf tethered fixed-wing unmanned aerial vehicles (UAVs). The non-optimized system demonstrated hover flight and slow vertical lifting (less than 1 m/s) capabilities while lifting a 20 kg payload with two 3.2 kg UAVs and a total of 2.1 kW. The lifting efficiency achieved by the GRS is approximately 4 times better than that of any conventional rotorcraft or heavy-lift VTOL system. The results of this study are promising and bring us closer to the reality of using available commercial airplanes for vertical lifting applications. The experimental setup, the control scheme, the flight test results, and a comparison of the GRS performance to that of conventional rotorcraft are described in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".