Path planning for multiple Unmanned Aerial Vehicles using genetic algorithms
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
In the future, autonomous Unmanned Aerial Vehicles (UAVs) need to work in teams to share information and coordinate activities. The private sector and government agencies have implemented UAVs for home-land security, reconnaissance, surveillance, data collection, urban planning, and geometrics engineering. Significant research is in progress to support the decision-making process for a Multi-Agent System (MAS) consisting of multiple UAVs. This paper investigates fundamental issues in path planning for multiple UAVs. MASs with multiple UAVs are typical distributed systems. We propose to use genetic algorithms to plan multiple paths for multiple UAVs. Simulation technologies have become important to the development of aerospace vehicles. In this research, we verify the proposed path planning approach using Matlab. Simulation results demonstrate that the proposed approach is able to plan multiple paths for UAVs successfully.
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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 | 0.000 | 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 | 0.000 |
| 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