Close‐range aerial inspection of large‐scale industrial facilities such as power plants is complex. It is characterized by a few areas suitable for safe landing, many obstacles, complicated radio communication, and so forth. The paper presented a new offline approach for automated mission planning in such conditions. It is based on novel concepts of ‐shaped flight profile and safe altitude map. These concepts reduce the mission planning task to a multiple traveling salesmen problem on a two‐dimensional map, which is solved using rapidly exploring random tree and genetic algorithms. A computational fluid dynamics‐based local turbulence map concept was proposed to avoid turbulence zones during inspection flights. As an advantage, this map can be generated at least 6.8× faster than the known approaches. Our new multicopter flight time prediction model provides an error of less than 10% in most cases and significantly outperforms those implemented in commercial mission planning software. All the proposed solutions were verified during a 2400‐MW power plant inspection. Our algorithms managed to plan to visit all the inspection points in six routes, approximately 15 min long each. This mission set has a comparable overall duration but better safety and even battery usage compared with the routes planned by a qualified operator. Moreover, the designed aerial inspection system provides higher quality images than manually from the ground using a thermal camera with two times higher sensitivity and equipped with more advanced optics. To our knowledge, this research is the first to report the successful implementation of autonomous unmanned aerial vehicle‐based power equipment diagnostics on a large‐scale thermal power plant.

中文翻译：

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空中大型电厂热检查的自动化任务规划

对发电厂等大型工业设施进行近距离空中检查非常复杂。其特点是适合安全着陆的区域少、障碍物多、无线电通信复杂等。该论文提出了一种新的离线方法，用于在这种情况下进行自动任务规划。它基于形状飞行剖面和安全高度图的新颖概念。这些概念将任务规划任务简化为二维地图上的多个旅行商问题，该问题可以使用快速探索随机树和遗传算法来解决。提出了基于计算流体动力学的局部湍流图概念，以避免检查飞行期间的湍流区域。其优点是，该图的生成速度至少比已知方法快 6.8 倍。我们新的多旋翼飞行时间预测模型在大多数情况下误差小于 10%，并且明显优于商业任务规划软件中实现的模型。所有提出的解决方案均在 2400 兆瓦发电厂检查期间得到验证。我们的算法成功地计划访问 6 条路线中的所有检查点，每条路线大约 15 分钟长。与合格操作员规划的路线相比，该任务集的总持续时间相当，但安全性更高，甚至电池使用率更高。此外，所设计的空中检查系统使用灵敏度高出两倍并配备更先进光学器件的热像仪，提供比地面手动检查更高质量的图像。据我们所知，这项研究首次报告了在大型火力发电厂成功实施基于自主无人机的电力设备诊断。