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.

翻译：本文探讨了为多架无人机规划最小能耗覆盖路径的问题。所研究的多无人机覆盖路径规划（mCPP）是许多无人机应用（如巡检和航空测量）中的关键问题。然而，现有方法通常以路径长度为目标，这既无法直接最小化能耗，也无法通过电池容量约束单条路径的能量消耗。为此，我们提出一种新颖的mCPP方法，该方法利用最优飞行速度以最小化单位距离能耗，并采用一种简单而精确的能耗估计算法，该算法在mCPP规划阶段使用。该方法通过往复式分解将给定区域分解，并将mCPP表示为具有最小能耗目标和能耗约束的多集合旅行商问题实例。实验表明，所提方法在计算时间和生成路径的能效方面均优于现有方法。结果显示，与真实飞行能耗相比，能耗估计的平均准确率达97%。通过两架无人机的真实覆盖实验验证了所提方法的可行性。