考虑飞行速度最优的多无人机覆盖任务能耗感知规划
Energy-aware Multi-UAV Coverage Mission Planning with Optimal Speed of Flight
AI总结：
本文提出一种多无人机覆盖路径规划方法，通过最优飞行速度和精确能耗估计最小化能耗，在计算时间、能耗效率上优于现有方法，并经双无人机实验验证。
摘要：
本文解决了为多无人机规划最小能耗覆盖路径的问题。所探讨的多无人机覆盖路径规划（mCPP）是许多无人机应用（如检查和航空测量）的关键问题。然而，现有方法典型的路径长度目标并不能直接最小化能耗，也不允许根据电池容量限制单条路径的能耗。为此，我们提出了一种新颖的 mCPP 方法，该方法使用最优飞行速度来最小化单位行驶距离的能耗，并采用一种简单而精确的能耗估计算法，在 mCPP 规划阶段加以利用。该方法利用牛耕式分解对给定区域进行分解，并将 mCPP 表示为具有最小能耗目标和能耗约束的多重集合旅行商问题的一个实例。结果表明，所提方法在计算时间和生成路径的能耗效率方面均优于现有最先进方法。实验结果表明，能耗估算的精度平均达到真实飞行能耗的 97%。所提方法的可行性已通过双无人机真实世界覆盖实验得到验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
