This paper considers the fundamental problem of P lacement of unmanned A erial vehicles achievi N g 3D D irectional cover A ge (PANDA), that is, given a set of objects with determined positions and orientations in a 3D space, deploy a fixed number of UAVs by adjusting their positions and orientations such that the overall directional coverage utility for all objects is maximized. First, we establish the 3D directional coverage model for both cameras and objects. Then, we propose a Dominating Coverage Set (DCS) extraction method to reduce the infinite solution space of PANDA to a limited one without performance loss. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint and present a greedy algorithm with $1-1/e$ approximation ratio to address this problem. We conduct simulations and field experiments to evaluate the proposed algorithm, and the results show that our algorithm outperforms comparison ones by at least 75.4%.

中文翻译：

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在3D空间中定向覆盖的无人飞行器的放置

本文考虑了基本问题 P 无人值守 一种 重型汽车 ñ g 3D d 常规封面 一种 ge（PANDA），也就是说，给定一组对象在3D空间中具有确定的位置和方向，可以通过调整其位置和方向来部署固定数量的UAV，以使所有对象的总体方向覆盖效用最大化。首先，我们为相机和物体建立3D定向覆盖模型。然后，我们提出了一种支配覆盖集（DCS）提取方法，以将PANDA的无限解空间减少到有限的空间而不会降低性能。最后，我们将重构问题建模为最大化受拟阵约束的单调子模函数，并提出具有 $ 1-1 / e $ 解决这个问题的近似比率。我们进行了仿真和野外实验，对所提算法进行了评估，结果表明，该算法的性能优于比较算法至少75.4％。