This work proposes a fast algorithm for generating obstacle-free and wind-efficient flight paths at a constant above-ground-level altitude in urban environments because a fast flight path planning algorithm is an essential function or service needed for enabling small unmanned aerial vehicle (sUAV) to operate in urban environments within Class G airspace. The proposed method first converts the 3D path planning problem to a 2D problem by constructing an obstacle map at a given above-ground-level altitude. A quad-tree decomposition is then used to build a search space in terms of obstacle occupancy and wind difference. The wind cost of traveling through each cell is defined based on energy consumption under various wind conditions. A repulsive potential is also adopted to make sure that the flight plans stay away from obstacles. The Theta* search algorithm, a variant of A* algorithm, is applied to mitigate the path angle change constraints introduced by grid-based graphs. With the Theta* and postsmoothing techniques, an obstacle-free, wind efficient, and constant above-ground-level flight plan can be quickly generated for sUAV operations in urban environments while meeting the lateral path angle constraints. The results showed that the path planning algorithm is efficient and can be finished within several seconds. With a proper choice of wind coefficient, the proposed path planning algorithm outperforms the multiple-shooting trajectory optimization method even in an obstacle-free environment. With the flexibility of incorporating other geo-related costs and computation efficiency, the proposed algorithm shows the potential for real-time flight path planning in complex urban environments.

这项工作提出了一种快速算法，用于在城市环境中以恒定的地上高度生成无障碍且高效风的飞行路径，因为快速飞行路径规划算法是启用小型无人飞行器所需的基本功能或服务（ sUAV）在G级空域内的城市环境中运行。所提出的方法首先通过在给定的地面高度上构建障碍图将3D路径规划问题转换为2D问题。然后，根据障碍物占用和风差，使用四叉树分解来构建搜索空间。遍历每个单元的风能成本是根据各种风况下的能耗定义的。还具有排斥力，以确保飞行计划远离障碍物。Theta *搜索算法是A *算法的一种变体，用于缓解基于网格的图所引入的路径角度变化约束。借助Theta *和后期平滑技术，可以在满足侧向航向角度约束的同时，快速生成无障碍，风力高效且恒定的地上飞行计划，以供城市环境中的sUAV操作使用。结果表明，该路径规划算法是有效的，并且可以在几秒钟内完成。通过正确选择风系数，即使在无障碍环境中，所提出的路径规划算法也优于多拍摄轨迹优化方法。通过灵活地合并其他与地理相关的成本和计算效率，