abstract = "Efficient power inspection is crucial for maintaining
a stable power system. During an inspection, unmanned
aerial vehicles (UAVs) usually need to be recharged due
to the wide geographical range of inspection and the
limited battery capacity of UAVs. This limitation makes
the problem more challenging that requires not only
optimising the task execution order, but also taking
the chargings of UAVs into consideration. In order to
address this complex problem, this work first
formulates the UAV power inspection planning problem
with charging stations. After that, we propose a new
heuristic navigation model, in which UAVs can follow a
heuristic rule to decide where to go next based on both
its own information and task-related information. To
obtain the heuristic rule, we design a set of features
to describe the status of the UAVs and task completion.
Then a genetic programming (GP) algorithm is introduced
to evolve and get the heuristic rule. Finally, by
applying heuristic navigation rule, the UAV navigation
model can automatically prioritize task and charging
order, and generate UAV flight routes that satisfy all
constraints. The experiment results show that our
method significantly outperforms the state-of-the-art
algorithms.",