This study proposes an innovative method for achieving autonomous flight to inspect overhead transmission facilities. The proposed method not only integrates multimodal information from novel sensors but also addresses three essential aspects to overcome the existing limitations in autonomous flights of an unmanned aerial vehicle (UAV). First, a novel deep neural network architecture titled the rotational bounding box with a multi‐level feature pyramid transformer is introduced for accurate object detection. Second, a safe autonomous method for the transmission tower approach is proposed by using multimodal information from an optical camera and 3D light detection and ranging. Third, a simple yet accurate control strategy is proposed for tracking transmission lines without necessitating gimbal control because it keeps the UAV's altitude in sync with that of the transmission lines. Systematic analyses conducted in both virtual and real‐world environments confirm the effectiveness of the proposed method. The proposed method not only enhances the performance of autonomous flight but also provides a safe operating platform for inspection personnel.

中文翻译：

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用于架空输电设施自主巡检的多模态无人机自主飞行策略

这项研究提出了一种实现自主飞行以检查架空传输设施的创新方法。所提出的方法不仅集成了来自新型传感器的多模态信息，而且还解决了三个基本方面的问题，以克服无人机（UAV）自主飞行的现有限制。首先，引入了一种新颖的深度神经网络架构，名为带有多级特征金字塔变换器的旋转边界框，以实现准确的目标检测。其次，通过使用来自光学相机的多模态信息以及 3D 光检测和测距，提出了一种用于输电塔方法的安全自主方法。第三，提出了一种简单而准确的控制策略来跟踪传输线，而无需万向节控制，因为它使无人机的高度与传输线的高度保持同步。在虚拟和现实环境中进行的系统分析证实了所提出方法的有效性。该方法不仅提高了自主飞行的性能，而且为检查人员提供了安全的操作平台。