Heading and position receding horizon control for trajectory generation
Bibliographic record
Abstract
This paper presents a comparative study of two formulations of model predictive control with receding horizons for the cooperative control of a team of unmanned aerial vehicles. In the first formulation, the vehicle trajectories are solved dynamically as sequences of vehicle headings over prediction horizons and executed over shorter action horizons. This formulation takes advantage of an implementation of collision avoidance based on vehicle heading constraints. In the second formulation, the vehicle trajectories are solved as sequences of vehicle positions, rather than vehicle headings. This formulation handles collision avoidance with vehicle position constraints. An efficient branch-and-bound algorithm is proposed to support the mixed integer constraints, and a collision avoidance solution based on heading constraints is evaluated. This paper shows that both receding horizon formulations produce exactly the same vehicle trajectories when they are used without collision avoidance constraint. It is shown however that heading receding horizon control requires less computing power than position receding horizon control whether in situations of collision avoidance or not.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".