Bibliographic record
Abstract
This chapter addressed the problem of autonomous flight control of a quadrotor UAV. It was conducted as a research project at the School of Information Technology and Engineering (S.I.T.E.), University of Ottawa. Detailed mathematical modeling of the quadrotor's kinematics and dynamics was provided. A modular fuzzy logic approach was proposed for the autonomous control of quadrotors in general, without the need for a precise mathematical model of their complex and ill-defined dynamics. The fuzzy technique was implemented through Mamdani and TSK inference engines for comparison purposes. The controller comprises six individual fuzzy logic modules designated for the control of the quadrotor's position and orientation. The investigation on the two types of control methodologies was conducted in a simulator environment, where disturbances such as wind conditions and sensor noise were incorporated for a more realistic simulation. The fuzzy flight controller was eventually implemented on a quadrotor test-bed specifically designed and built for the project. The experiments were first conducted on the simulator before being validated on the testbed. The results demonstrated a successful control performance especially with the Mamdani inference engine. When compared to other conventional techniques applied for a similar purpose Altug et al. (2002), Bouabdallah et al. (2004b), the proposed methodology showed a higher robustness despite the induced disturbances. The future work is directed towards achieving fully autonomous flight in outdoor environments. Furthermore, adaptive fuzzy control techniques will be investigated to automatically tune some of the controller's parameters online, to further optimize its performance.
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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) | 0.000 | 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) | 0.001 | 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".