Autonomous Navigation of Unmanned Aerial Vehicles Subjected to Time Delays
Bibliographic record
Abstract
This thesis presents a new method of compensating for time delays in the control and navigation of UAVs.The aerial vehicles are controlled with a coordinated lateral control.The operator signals are delayed and a bank of recursive least squares (RLS) filters are used to identify the delay and the target waypoint.Hypothesis testing is implemented to select the filter that most closely matches the delay.This filter determines the delay and the target waypoint.Once a filter is selected, the UAV then computes its heading to the estimated target waypoint.By executing the self computed heading, the UAV performs autonomous navigation to the target waypoint.The operator keeps operating the UAV and the UAV keeps track of the operator commands so that if there is a change in delay or the waypoint, the UAV learns and adjusts accordingly.iii My deepest gratitude to my thesis supervisor Prof.Howard Schwartz for his patience and guidance in this thesis.To you I say a very big thank you for always making yourself available to answer my questions and dispel my confusions.I like to thank my co-supervisor Prof. Givigi
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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".