Time-optimal path planning in high winds for a turning rate constrained UAV is a challenging problem to solve and is important for deployment and field operations. Previous works have used trochoidal path segments, which consist of straight and maximum-rate turn segments, as optimal extremal paths in uniform wind conditions. Current methods iterate over all candidate trochoidal trajectory types and choose the time-optimal one; however, this exhaustive search can be computationally slow. In this paper we present a method to decrease the computation time. We achieve this via a geometric approach to reduce the candidate trochoidal trajectory types by framing the problem in the air-relative frame and bounding the solution within a subset of candidate trajectories. This method reduces overall computation by 37.4% compared to pre-existing methods in Bang-Straight-Bang trajectories, freeing up computation for other onboard processes and can lead to significant total computational reductions when solving many trochoidal paths. When used within the framework of a global path planner, faster state expansions help find solutions faster or compute higher-quality paths. We also release our open-source codebase as a C++ package.

翻译：在高风速条件下对具有转向速率约束的无人机进行时间最优路径规划是一个具有挑战性的问题，且对实际部署与现场作业至关重要。已有研究将摆线路径段（由直线段和最大速率转向段组成）作为均匀风场条件下的最优极值路径。现有方法需遍历所有候选摆线轨迹类型并选择时间最优者，但这种穷举搜索可能造成计算延迟。本文提出一种降低计算时间的几何方法，通过将问题转换到空气相对坐标系，并将解约束在候选轨迹子集中，从而减少待评估的候选摆线轨迹类型。与现有Bang-Straight-Bang轨迹方法相比，本方法可降低37.4%的整体计算量，为机载其他处理流程释放计算资源，并在求解多条摆线路径时可显著降低总计算量。当嵌入全局路径规划框架时，快速的状态扩展有助于更快地找到可行解或生成更高品质的路径。我们同时以C++程序包形式开源了相关代码库。