Use of a DJI Tello Drone as an Educational Platform in the Field of Control Engineering
Bibliographic record
Abstract
This paper presents a hands-on pedagogical approach using a DJI Tello drone as an interactive teaching platform in the field of automatic control engineering. The DJI Tello is a small commercial quadcopter drone and includes a software development kit (SDK) that allows developers to control the Tello using various programming languages, including Python. The drone is also equipped with a large number of sensors that can be used in real-time to collect data and analyze how changes in control inputs such as thrust, pitch, roll, and yaw affect its flight path and stability. These features make the Tello a good teaching tool for demonstrating control concepts in an attractive and practical way. Two examples of pedagogical applications are presented in this paper. The first example aims to illustrate in practice how system identification can be used to create a mathematical model of the DJI Tello drone using transfer functions. The second example aims to illustrate how to design a Proportional-Integral (PI) controller and validate it after its implementation on the DJI Tello drone. Through these teaching demonstrations, it was possible to enhance cognitive learning while providing students with a better understanding of the fundamental concepts of modeling and control. It was also observed that even though the students had no background in aeronautics, using an atypical system such as a drone aroused their curiosity, encouraging them to participate, thus making the in-class demonstrations more dynamic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".