  We present a multi-UAV Coverage Path Planning (CPP) framework for the
inspection of large-scale, complex 3D structures. In the proposed
sampling-based coverage path planning method, we formulate the multi-UAV
inspection applications as a multi-agent coverage path planning problem. By
combining two NP-hard problems: Set Covering Problem (SCP) and Vehicle Routing
Problem (VRP), a Set-Covering Vehicle Routing Problem (SC-VRP) is formulated
and subsequently solved by a modified Biased Random Key Genetic Algorithm
(BRKGA) with novel, efficient encoding strategies and local improvement
heuristics. We test our proposed method for several complex 3D structures with
the 3D model extracted from OpenStreetMap. The proposed method outperforms
previous methods, by reducing the length of the planned inspection path by up
to 48%
