An Improved Genetic Algorithm for Rapid UAV Path Planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract UAV technologies have advanced rapidly and are widely used in military and civilian fields. For instance, the UAV swarms have been widely applied to oil and gas exploration, geometric mapping, and cargo transport. However, the UAV swarm system requires a more accurate plan before performing any mission. Path planning is one of the essential parts of mission planning because of the higher requirement of robustness and real-time communication. UAV path planning could generate the optimal path starting from the current position to the target in an environment with an obstacle. While the standard genetic algorithm has lacked efficiency in the iteration process and poor stability, a new genetic operator is proposed for the genetic algorithm and applied to the path planning simulation of UAV swarms in this study. A three-dimensional mapping and fitness function are already constructed for the simulation. The simulation result shows the algorithm with improved selection and mutation operator can efficiently and stably converge to the optimal solution.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 it