Design and Fabrication of a Flapping-Wing Robot Based on Slider-Crank Mechanism
Bibliographic record
Abstract
Tailless Flapping-Wing Micro Air Vehicles (FW-MAVs) have gained more attention recently because they utilize energy more efficiently compared to fixed-wing aircraft and rotorcrafts. FW-MAVs could be used commercially to explore confined spaces with insufficient air or serve as surveillance robots. However, due to their use of unsteady aerodynamics and small size, the research and design process is very complicated. In this paper, I propose a flight mechanism for a light-weighted, two-winged, hummingbird-inspired flapping-wing robot. Five versions of the robot were built; each version improved upon the issues of the previous one. Calculations were performed to optimize the stroke amplitude and the transmission ratio of the gears. Four groups of control experiments were conducted to investigate the relationship between different factors (voltage, motor type, wing area, and the number of veins) and the robot’s lift, which was monitored by a pressure sensor. I analyzed the results from the experiments and built a final version of the robot based on a slider-crank mechanism. The main structure of the final version is made of three 3mm carbon fiber boards, and the wings are made of 0.025mm PET (polyethylene terephthalate) material, reinforced by three carbon fiber rods: two 0.5mm ones across the membrane and a 1mm one at the leading edge. The robot weighs 16.3g and can produce enough lift to overcome its gravity under 9V with an off-board power source by exhibiting an upward trend during a tethered flight test.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".