一种基于维诺划分与虚拟力的多无人机部署优化算法(复杂低空电磁环境认知与智能网联专题)

      A Multi-UAV Deployment Optimization Algorithm Based on Voronoi Partitioning and Virtual Force

      • 摘要: 为解决当前多无人机部署优化算法在覆盖效率上的不足,本文提出了一种基于维诺划分与虚拟力的无人机部署优化算法(Voronoi Partitioning and Virtual Force-based UAV Deployment, VVF)。具体而言,算法首先在初始随机部署的基础上,通过维诺划分将目标区域划分为多个子区域,并在每个子区域上构建局部覆盖模型。接着,在每个局部覆盖模型中,算法采用改进的虚拟力机制调整无人机位置,高效消除覆盖空洞。最后,评估当前部署结果并剔除冗余无人机。算法迭代优化,直至获得最优的无人机部署位置和数量。与PSOVP、EVF和VEVF等基线相比,VVF在覆盖效率上提升了6.5%,在收敛效率上提升了70.69%。

         

        Abstract: To address the limitations of current multi-UAV deployment optimization algorithms in terms of coverage efficiency, this paper proposes a UAV deployment optimization algorithm based on Voronoi partitioning and Virtual Force (VVF). Specifically, the algorithm first divides the target area into multiple sub-regions using Voronoi partitioning based on an initial random deployment, and constructs a local coverage model for each sub-region. Then, in each local coverage model, the algorithm uses an improved virtual force mechanism to adjust the UAV positions and efficiently eliminate coverage holes. Finally, the current deployment result is evaluated, and redundant UAVs are removed. The algorithm iteratively optimizes until the optimal deployment scheme is achieved. Compared to baselines such as PSOVP, EVF, and VEVF, VVF improves coverage efficiency by 6.5% and convergence efficiency by 70.69%.

         

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