  This paper tackles the problem of planning minimum-energy coverage paths for
multiple UAVs. The addressed Multi-UAV Coverage Path Planning (mCPP) is a
crucial problem for many UAV applications such as inspection and aerial survey.
However, the typical path-length objective of existing approaches does not
directly minimize the energy consumption, nor allows for constraining energy of
individual paths by the battery capacity. To this end, we propose a novel mCPP
method that uses the optimal flight speed for minimizing energy consumption per
traveled distance and a simple yet precise energy consumption estimation
algorithm that is utilized during the mCPP planning phase. The method
decomposes a given area with boustrophedon decomposition and represents the
mCPP as an instance of Multiple Set Traveling Salesman Problem with a minimum
energy objective and energy consumption constraint. The proposed method is
shown to outperform state-of-the-art methods in terms of computational time and
energy efficiency of produced paths. The experimental results show that the
accuracy of the energy consumption estimation is on average 97% compared to
real flight consumption. The feasibility of the proposed method was verified in
a real-world coverage experiment with two UAVs.
