| Autor: |
Qiwen Lu, Yifeng Qiu, Chaotao Guan, Haoyu Wang, Mengqi Zhu, Biao Xu, Wenji Li, Zhun Fan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Applied Sciences, Vol 13, Iss 19, p 10920 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app131910920 |
| Popis: |
This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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