Hybrid Framework for UAV Mission Planning Using Logic-Based Imaging and Reinforcement Learning
Bibliographic record
Abstract
This study proposes an Unmanned Aerial Vehicle(UAV) mission planning framework that integrates Logic-Driven Imaging Planning (LDIP) with Reinforcement Learning-Based Path planning(RLPP). The proposed framework consists of the Mission-Specific Imaging Library (MSIL), which generates and stores mission-specific parameters, a module for calculating the Sweep Effective Flight Area (SEFA) based on Detectable Area(DA), the LDIP module to ensure image quality, and the RLPP module for trajectory optimization. In particular, the LDIP module generates imaging plans based on the given mission context, while the RLPP module optimizes the UAV path within the SEFA. Each module is designed to be adaptive to dynamic environments by considering AI performance, camera specifications, and UAV platform capabilities. The simulation results confirm that the proposed approach improves mission efficiency compared to conventional methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".