论文标题
多uav覆盖路径计划，以检查大型和复杂的结构
Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures
论文作者
论文摘要
我们提出了一个多功能覆盖路径计划（CPP）框架，以检查大规模，复杂的3D结构。在提出的基于抽样的覆盖路径计划方法中，我们将多AVENT应用程序制定为多代理覆盖路径计划问题。通过结合两个NP安全问题：设置覆盖问题（SCP）和车辆路由问题（VRP），制定了设定的车辆路由问题（SC-VRP），并随后通过新颖的，高效的，高效的编码策略和本地改进的启发式方法，由修改后的随机关键遗传算法（BRKGA）解决，并通过修改后的随机关键关键遗传算法（BRKGA）解决。我们使用从OpenStreetMap提取的3D模型测试了几种复杂3D结构的建议方法。提出的方法通过将计划的检查路径的长度降低多达48％来优于先前的方法
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%
