Optimal Longitudinal Trajectory Planning for a Quadrotor UAV Including Linear Drag Effects
Bibliographic record
Abstract
This work proposes a methodology for determining the optimal trajectories of a quadrotor UAV in the sense of a trade-off between a control energy cost and a time-related cost under the influence of linear drag. The consideration of such a trade-off is motivated by the need to find efficient strategies to deal with battery-powered UAVs having limited battery charge. The problem is formulated as a free terminal time optimal control problem using a trade-off cost index and solutions are derived using the Pontryagin's Minimum Principle (PMP). The results show the sensitivity to drag effects of some important quantities such as trajectory smoothness, control energy, and final time. One of the important contributions of the methodology proposed in this paper is that it provides a Pareto optimal curve that is parameterized by the drag coefficient divided by the mass of the vehicle. Extensive simulation plots, including the Pareto optimal curve, show the results of the proposed method for different drag coefficients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".