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) collision 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.

翻译：无人机有望通过提升生产率、缩短巡检时间、改善数据质量并消除人工操作风险，彻底改变电力线巡检方式。当前最先进的电力线巡检系统存在两大缺陷：（i）控制与感知解耦，需要关于电力线和杆塔位置的精确信息；（ii）避障与电力线跟踪解耦，导致在杆塔附近跟踪效果不佳，进而降低视觉巡检的数据质量。本研究提出一种模型预测控制器（MPC），通过紧密耦合感知与动作来克服上述局限。该控制器生成的指令既能最大化电力线的可见性，同时又能安全避开电力杆塔。针对电力线检测，我们提出一种轻量级基于学习的检测器，该检测器仅基于合成数据训练，可零样本迁移至真实电力线图像。通过在仿真环境和搭建的电力线基础设施模型上开展实验，验证了本系统的有效性。