Bibliographic record
Abstract
This research develops an intelligent UAV swarm scheduling algorithm to optimize urban infrastructure inspection processes by minimizing inspection time while ensuring comprehensive coverage. We formulate the challenge as a mixed integer non-linear programming problem and propose a decomposition approach addressing three critical components: structure-specific path planning, market-based task allocation, and conflict-free scheduling. Our methodology integrates these components through an iterative process within a hybrid centralized-decentralized architecture tailored for urban environments. Simulation results demonstrate that our algorithm reduces inspection time by 35% compared to single-UAV approaches while maintaining 98% coverage completeness. The approach exhibits 40% improved energy efficiency in limited-battery scenarios and polynomial-time computational complexity that scales efficiently with increasing swarm size. The algorithm typically converges within 3-5 iterations to near-optimal solutions. The proposed framework successfully balances inspection quality and resource efficiency while adapting to urban-specific challenges, including GPS degradation, obstacle avoidance, and structural complexity. Structure-specific inspection patterns significantly enhance efficiency across different infrastructure elements. This research advances UAV-based infrastructure monitoring capabilities, offering potential benefits for maintenance planning, public safety, and urban resilience. The computational efficiency makes the solution suitable for deployment on resource-constrained platforms typical in UAV applications.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".