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：无人机具有通过提高生产率、缩短检测时间、改善数据质量以及消除人工操作员风险来革新电力线检测的潜力。当前最先进的电力线检测系统存在两个不足：（i）控制与感知相解耦，需要精确的电力线和杆塔位置信息；（ii）避障与电力线跟踪相解耦，导致在电力杆塔附近跟踪效果不佳，进而降低视觉检测的数据质量。在本研究中，我们提出了一种模型预测控制器（MPC），通过紧密耦合感知与行动来克服这些限制。该控制器生成的指令能够在最大化电力线可见性的同时，安全避开电力杆塔。在电力线检测方面，我们提出了一种轻量级基于学习的检测器，该检测器仅使用合成数据进行训练，并能够零样本迁移至真实世界的电力线图像。我们通过模拟实验和真实场景中的电力线基础设施模拟验证了该系统。