Drones have the potential to revolutionize power line inspection by increasing productivity, reducing inspection time, improving data quality, and eliminating the risks for human operators. Current state-of-the-art systems for power line inspection have two shortcomings: (i) control is decoupled from perception and needs accurate information about the location of the power lines and masts; (ii) obstacle avoidance is decoupled from the power line tracking, which results in poor tracking in the vicinity of the power masts, and, consequently, in decreased data quality for visual inspection. In this work, we propose a model predictive controller (MPC) that overcomes these limitations by tightly coupling perception and action. Our controller generates commands that maximize the visibility of the power lines while, at the same time, safely avoiding the power masts. For power line detection, we propose a lightweight learning-based detector that is trained only on synthetic data and is able to transfer zero-shot to real-world power line images. We validate our system in simulation and real-world experiments on a mock-up power line infrastructure. We release our code and datasets to the public.

翻译：无人机有望通过提高生产力、缩短检查时间、改善数据质量以及消除操作人员安全风险，彻底革新电力线巡检方式。当前最先进的电力线巡检系统存在两个缺点：(i) 控制与感知解耦，需要精确的电力线和杆塔位置信息；(ii) 避障与电力线跟踪解耦，导致在电力杆塔附近跟踪效果不佳，进而降低视觉检查的数据质量。本研究提出一种模型预测控制器（MPC），通过紧密耦合感知与行动来克服上述局限。该控制器生成的指令能在最大化电力线可见性的同时，安全避开电力杆塔。为检测电力线，我们提出一种轻量级基于学习的检测器，该检测器仅使用合成数据训练，能以零样本方式迁移至真实电力线图像。我们通过仿真实验和真实电力线模拟设施验证了系统性能，并将代码与数据集公开发布。