Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures
This work addresses the challenge of efficient inspection for large-scale infrastructure, though it appears incremental as it builds on existing path planning and optimization techniques.
The paper tackles the problem of inspecting large and complex 3D structures using multiple UAVs by developing a multi-UAV coverage path planning framework, resulting in a reduction of the planned inspection path length by up to 48% compared to previous methods.
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%