混合燃料无人机的噪声感知路径规划与功率管理
Noise Aware Path Planning and Power Management of Hybrid Fuel UAVs
AI总结：
本文研究混合燃料无人机的噪声感知路径规划与功率管理，提出MILP模型并设计可证明伪多项式时间的标签校正算法，实验显示其可在数秒内处理两万节点实例。
摘要：
混合燃料无人机（UAV）通过组合多种能源，相比标准的单一燃料源配置具有若干优势，其中最主要的是航程和效率的提升。多种电力或燃料源还能够缓解每种能源各自的明显缺陷，同时在任务或路径规划中发挥其优势。本文考虑一种配备内燃机-发电机和电池组作为能源的无人机。我们以噪声感知的方式研究该平台的路径规划与功率管理。为求解路径规划问题，我们首先给出该问题的混合整数线性规划（MILP）表述。随后，我们提出并分析一种标签校正算法，并证明其具有伪多项式运行时间。文中给出了大量数值测试结果，针对不同图结构、问题参数和搜索启发式方法，分析了该标签算法的性能与可扩展性。结果表明，该算法能够在仅几秒内求解包含多达两万个节点的图上的实例。
英文摘要：
Hybrid fuel Unmanned Aerial Vehicles (UAV), through their combination of multiple energy sources, offer several advantages over the standard single fuel source configuration, the primary one being increased range and efficiency. Multiple power or fuel sources also allow the distinct pitfalls of each source to be mitigated while exploiting the advantages within the mission or path planning. We consider here a UAV equipped with a combustion engine-generator and battery pack as energy sources. We consider the path planning and power-management of this platform in a noise-aware manner. To solve the path planning problem, we first present the Mixed Integer Linear Program (MILP) formulation of the problem. We then present and analyze a label-correcting algorithm, for which a pseudo-polynomial running time is proven. Results of extensive numerical testing are presented which analyze the performance and scalability of the labeling algorithm for various graph structures, problem parameters, and search heuristics. It is shown that the algorithm can solve instances on graphs as large as twenty thousand nodes in only a few seconds.
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。
