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To ensure of continuous communication coverage of ground mobile users by UAV-swarm clusters in disaster relief scenarios, a UAV-swarm cluster path optimization algorithm was designed based on deep reinforcement learning of multiple intelligences.The UAVs in this algorithm framework had distributed decision making capability, and could dynamically adjust their own movement strategy according to the user′s movement. The UAVs should be deployed to maximize long-term coverage of ground mobile users in a specified area by setting appropriate reinforcement learning rewards and parameters while satisfying multiple constraints such as coverage percentage, collision avoidance, and energy constraints. The proposed model was compared with other UAV deployment scheme algorithms by simulation. Results showed that the model significantly improved in terms of convergence speed and convergence effect.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2023017
China Classification Code:TP18;TN929.5
Citation Information:
[1]ZHANG Bo,YANG Kunhao,LI Junfeng.Aerial Base Station Deployment Optimization Based on Deep Reinforcement Learning[J].Journal of Zhengzhou University(Natural Science Edition),2024,56(04):28-33.DOI:10.13705/j.issn.1671-6841.2023017.
Fund Information:
国家自然科学基金面上项目(61972092); 郑州市协同创新重大专项(20XTZX06013)
2023-01-16
2023
2023-09-18
2025-03-18
2025
3
2023-09-27
2023-09-27
2023-09-27