  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.
