Develop and Design Small Scale UAV
Bibliographic record
Abstract
Nowadays, many people want to buy a small-scale unmanned aerial vehicle (UAV) either for recreational purposes, photography and video editing or for air surveillance. The main issue with conventional small-scale UAVs is that they require experience to operate them. Besides that, the control system also plays important role for its flight stability and endurance for small-scale UAV. The weight of the load and travelling speed also being issued for small-scale UAV. As a result, three objectives were formulated based on the problem statement which are to fabricate the appropriate size of a small-scale UAV in term of its mass and frame size; to design the control system and increase the stability of the small-scale UAV; and to test the flight endurance of the small-scale UAV. This research is conducted through the following methodology, which is designing the quadcopter body mainframe, constructing the circuit diagram, developing the RC transmitter and receiver for Arduino and testing the UAV functionality and flight test. Softwares that were involved in this project are Solidworks, Arduino compiler, Proteus, Processing and Matlab. The equipments that are used are Arduino UNO, MPU-6050 sensor, Li-Po 11.1V battery, Electronic Speed Controller (ESC), brushless DC motor, 2.4 GHZ RC transmitter and 6-channel receiver. The limitation also being found from those experiments. Finally, the results and discussion will show and explain about the early sketch for quadcopter body mainframe including dimension for each part and stress-strain diagram using Solidworks. It also includes the simulation and hardware connection for brushless DC motor and MPU-6050 sensor using Proteus, Arduino compiler, Processing and Matlab software.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".