UAV path planning in presence of occlusions as noisy combinatorial multi-objective optimisation
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
A realistic noisy combinatorial problem on surveillance by unmanned aerial vehicle (UAV) in presence of weather factors is defined. The presence of cloud coverage is considered as a posterior Gaussian noise in the visibility region of the UAV. Recent studies indicate that recombination-based search mechanisms are helpful in solving noisy combinatorial problems. The search strategy of univariate marginal distribution algorithm that includes only selection and recombination, which has a close association with genepool crossover, proves to be beneficial in solving constrained and multi-objective combinatorial problems in presence of noise. This paper proposes a solution methodology based on multi-objective UMDA (moUMDA) with diversification mechanisms for the multi-objective problem of UAV surveillance. To obtain a well-spread set of Pareto optimal solutions, relevant diversification mechanisms are important. Numerical simulations show that moUMDA with and without K-means clustering provides better quality solutions and a more diverse Pareto optimal set than NSGA-II in solving this noisy problem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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