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Wind-energy based path planning for Unmanned Aerial Vehicles using Markov Decision Processes

机译:利用马尔可夫决策过程的无人空中车辆的风能路径规划

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Exploiting wind-energy is one possible way to extend the flight duration of an Unmanned Aerial Vehicle. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain, time-varying wind fields and plan a path through them that exploits the energy the field provides. A Gaussian distribution is used to determine uncertainty in the time-varying wind fields. We use a Markov Decision Process to plan a path based upon the uncertainty of the Gaussian distribution. Simulation results are presented to compare the direct line of flight between a start and target point with our planned path for energy consumption and time of travel. The result of our method is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.
机译:利用风能是延长无人驾驶飞行器的飞行持续时间的一种可能方法。 风能也可用于最小化计划路径的能量消耗。 在本文中,我们考虑不确定,时变风场,并通过它们利用现场提供的能量来平衡它们的路径。 高斯分布用于确定时变风场中的不确定性。 我们使用马尔可夫决策过程来基于高斯分布的不确定性来规划路径。 提出了模拟结果,以比较了我们计划的能耗和旅行时间的计划路径与目标点之间的直接飞行线。 我们的方法的结果是使用最多访问的单元格的强大路径,同时对每个单元中的风电场的高斯分布进行采样。

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