Three-dimensional path planning and collision-free flight control for drone-assisted autonomous pollination systems
Bibliographic record
Abstract
In this paper, the authors’ previous work regarding a conceptual drone-assisted autonomous pollination system (APS) is extended with regards to path planning and flight control. The APS Path Planning module is extended to optimize the path for missions requiring three-dimensional (3D) path planning, such as the pollination of almond trees. A new method of simplifying the 3D path planning problem by selecting cells or groups of flowers to visit is shown in this paper. This method is numerically demonstrated based on a simulated almond tree. The Flight Control module is extended to incorporate drag into a novel convex-optimization-based flight controller and a new method of collision avoidance called control sequence stitching (CSS). A linear drag model is integrated into the flight control formulation, which is validated through a simulated test flight. The concept of CSS is developed and explained as a method to generate seamless flight trajectories, while still reaping the benefits of convex optimization. This method can be used to generate collision-free trajectories and control commands rapidly for potential real-world APS missions.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".