Fli-Bi UAV: A Unique Surveying VTOL for Overhead Intelligence
Bibliographic record
Abstract
Vertical takeoff and landing (VTOL) systems have expanded the applications of unmanned aerial vehicles significantly. Combining the deployability of a vertically capable multirotor with the efficiency and flight speed of a fixed wing is desirable for mapping and surveying. The customer required a VTOL to conduct large-scale magnetometer surveys in Canadian forests, also seen as locations that are inaccessible by foot. No platforms on the market currently meet their requirements, so a unique solution has been developed. Weight is a massively limiting factor for aerial vehicles. With a payload requirement of 2.2 pounds, the all-up weight of the vehicle was targeted to be 20 pounds. Lightweight PLA filament was used for the wings and empennage, reducing the weight by almost 50% compared to regular PLA. Key structural components, such as the twin booms and wing spars, are constructed from carbon for airframe rigidity. With an emphasis on propulsion redundancy, the Fli-Bi UAV has an X8 configuration, employing eight motors for full control in vertical and horizontal flight. This provides enough redundancy to maintain flight if there was a failure at any single point in the propulsion system. Each motor is angled relative to the ground to provide supplemental lift in horizontal flight and assist the transition between flight modes. This provides enough thrust and lift to meet the 60-knot speed requirement. By eliminating servo-actuated control surfaces, motors and electronic speed controllers are the only points of failure in the propulsion and control system. The number and configuration of motors makes this a viable solution to fix the customer’s need for a redundant surveying VTOL. Many worst-case scenario assumptions were made when calculating payload capability, forward drag, and required thrust, which should pave the way for successful flight. There are multiple research opportunities available that this platform could be the basis for in the future.
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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".