Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. However, considering the enormous scale and complex nature of infrastructure, automation is essential for improving the efficiency and quality of inspection operations. One of the core problems in this regard is electing an optimal automated flight path that can achieve the mission objectives while minimizing flight time. This paper presents an effective formulation for the path planning problem in the context of structural inspections. Coverage is guaranteed as a constraint to ensure damage detectability and path length is minimized as an objective, thus maximizing efficiency while ensuring inspection quality. A two-stage algorithm is then devised to solve the path planning problem, composed of a genetic algorithm for determining the positions of viewpoints and a greedy algorithm for calculating the poses. A comprehensive sensitivity analysis is conducted to demonstrate the proposed algorithm's effectiveness and range of applicability. Applied examples of the algorithm, including partial space inspection with no-fly zones and focused inspection, are also presented, demonstrating the flexibility of the proposed method to meet real-world structural inspection requirements. In conclusion, the results of this study highlight the feasibility of the proposed approach and establish the groundwork for incorporating automation into UAS-based structural inspection mission planning.

翻译：无人航空系统（UAS）在基础设施检测领域的应用日益广泛。然而，考虑到基础设施的巨大规模和复杂特性，自动化对于提升检测作业的效率和质量至关重要。其中核心问题之一在于选择最优自动化飞行路径，使其既能完成检测任务目标，又能最小化飞行时间。本文针对结构检测场景下的路径规划问题，提出了一种有效的问题表述方法。该方法将覆盖率作为约束条件以保证损伤可检测性，同时以路径长度最小化为优化目标，从而在确保检测质量的前提下最大化作业效率。进而设计了两阶段算法来解决该路径规划问题：第一阶段采用遗传算法确定观测点位置，第二阶段采用贪心算法计算观测姿态。通过全面的灵敏度分析，验证了所提出算法的有效性和适用范围。文中还给出了算法的应用实例，包括含禁飞区的局部空间检测和聚焦检测，展示了该方法满足实际结构检测需求的灵活性。研究结果表明，该方法的可行性已得到验证，为在基于UAS的结构检测任务规划中引入自动化奠定了基础。