Multi-UAV Trajectory Optimization Under Dynamic Threats: An Enhanced GWO Algorithm Integrating a Priori and Real-Time Data
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| Publicado en: | International Journal of Computational Intelligence Systems vol. 18, no. 1 (Dec 2025), p. 140 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1875-6891 | ||
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| 024 | 7 | |a 10.1007/s44196-025-00863-y |2 doi | |
| 035 | |a 3267068422 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Zhou, Zihan |u Dalian University of Technology, Institute for Advanced Intelligence, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0000 9247 7930) | |
| 245 | 1 | |a Multi-UAV Trajectory Optimization Under Dynamic Threats: An Enhanced GWO Algorithm Integrating a Priori and Real-Time Data | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Though the widespread use of multi-UAV systems offers significant tactical and operational advantages, achieving efficient and secure collaborative planning remains a critical challenge in dynamic threat environments. Traditional methods struggle to balance path optimization with threat avoidance, particularly in fluctuating environments where UAVs must adapt to changing threats. To address this, an enhanced Grey Wolf Optimization (GWO) algorithm is proposed for multi-UAV collaborative planning in dynamic threat zones. Our research integrates a priori knowledge of threat zone locations, speeds, and directions with real-time data on the UAVs position relative to the threat zones to effectively manage dynamic threat zones, allowing UAVs to dynamically decide whether to navigate around or through these zones, thus significantly reducing trajectory costs. To further improve search efficiency and solution quality, strategies such as greedy initialization and K-means clustering are incorporated, enhancing the algorithms multi-objective optimization capabilities. Experimental results demonstrate that the dynamic threat zone crossing strategy significantly reduces trajectory costs compared to the traditional bypass strategy. Furthermore, the enhanced GWO algorithm outperforms both the traditional GWO and MP-GWO algorithms in terms of trajectory cost and convergence accuracy. Our approach provides novel insights and methodologies for the advancement of multi-UAV collaborative trajectory planning, while extending the applicability of the GWO algorithm in complex environments | |
| 653 | |a Collaboration | ||
| 653 | |a Strategy | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Trajectory optimization | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Clustering | ||
| 653 | |a Planning | ||
| 653 | |a Altitude | ||
| 653 | |a Algorithms | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Real time | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Trajectory planning | ||
| 653 | |a Vector quantization | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Guo, Yanhong |u Dalian University of Technology, Institute for Advanced Intelligence, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0000 9247 7930) | |
| 700 | 1 | |a Wang, Yitao |u Operation Software and Simulation Institute, Dalian Naval Academy, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0004 1759 9427) | |
| 700 | 1 | |a Lyu, Jingfan |u Dalian University of Technology, Institute for Advanced Intelligence, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0000 9247 7930) | |
| 700 | 1 | |a Gong, Haoran |u Dalian University of Technology, Institute for Advanced Intelligence, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0000 9247 7930) | |
| 700 | 1 | |a Ye, Xin |u Dalian University of Technology, Institute for Advanced Intelligence, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0000 9247 7930) | |
| 700 | 1 | |a Li, Yachao |u Xidian University, School of Electronic Engineering, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) | |
| 773 | 0 | |t International Journal of Computational Intelligence Systems |g vol. 18, no. 1 (Dec 2025), p. 140 | |
| 786 | 0 | |d ProQuest |t Computer Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3267068422/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3267068422/fulltext/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3267068422/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |