Intelligent UAV swarm scheduling algorithm for urban inspection task
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| Titel: | Intelligent UAV swarm scheduling algorithm for urban inspection task |
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| Autoren: | Haoyu Xu |
| Quelle: | Edelweiss Applied Science and Technology. 9:2751-2767 |
| Verlagsinformationen: | Learning Gate, 2025. |
| Publikationsjahr: | 2025 |
| Beschreibung: | 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. |
| Publikationsart: | Article |
| ISSN: | 2576-8484 |
| DOI: | 10.55214/25768484.v9i4.6650 |
| Dokumentencode: | edsair.doi...........1ffa44d4890eef9dea2c6f138b4d2343 |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | 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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| ISSN: | 25768484 |
| DOI: | 10.55214/25768484.v9i4.6650 |