Abstract
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%.
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