A Hybrid Optimal Path Planner for Parafoiled Systems
Bibliographic record
Abstract
When dealing with flight mechanics, minimization of nonlinearly constrained functions is often required for solving optimal control problems. This paper proposes a new dynamic programming method based on the Bellman equations with a novel state-space model. The effectiveness of this method is demonstrated through its integration into a hybrid algorithm for generating efficient guidance strategies for parafoils. The objective is to guide the parafoil system towards a circular target, from fixed initial coordinates and deployment velocity. Our algorithm aims to minimize energy consumption and optimize the time and distance over which the system can travel accurately. The nominal trajectory is obtained using a nonlinear shooting method based on the Pontryagin minimum, and the dynamic programming algorithm is used to generate new trajectories if the system deviates from the nominal one due to environmental conditions. The optimality of the proposed controller is evaluated using a 3D simulator with wind and gusts. The algorithm is subsequently optimized for deployment and implemented in a low-level language, the performances are evaluated and show more than adequate efficiency for embedded systems. The results demonstrate both the efficiency of the new dynamic approach by comparing it with nonlinear shooting; and the complementarity and efficiency of the two methods in guiding parafoil systems under real wind conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".