Multiple aerial/ground vehicles coordinated spraying using reinforcement learning
Bibliographic record
Abstract
Investments in unmanned aerial vehicles (UAVs) have recently surged in precision agriculture. However, multi-UAV missions can face limitations due to weather conditions, highlighting the need for effective spray coverage. A novel system tailored for spraying in windy conditions to tackle this challenge is proposed. Instead of directly controlling sprayed drops, the location of spraying UAVs based on real-time wind data is adjusted. Our proposed methodology consists of three stages: Firstly, on-policy reinforcement learning (RL) with Proximal Policy Optimization (PPO) is utilized to optimize path planning. In the second stage, another PPO iteration to correct wind drift is employed, leveraging the latest wind data to enhance spray mission efficiency. Lastly, a novel algorithm is introduced to improve efficiency in narrow areas by substituting unmanned aerial vehicles with unmanned ground vehicles. To evaluate the efficiency of the proposed aerial spraying system, we conducted a simulation and reported the corresponding results. • A novel wind effect corrector using RL for precision agriculture spraying efficiency • A hybrid RL model leveraging discrete and continuous outputs to tackle spray drift efficiently • An innovative planning feature combining UGV and UAV strengths to address the wind factor
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".