城市空中交通的障碍物感知四维轨迹设计
Obstacle-Aware Four-Dimensional Trajectory Design for Urban Air Mobility
浏览论文内容
中文总结 AI 辅助
研究城市空中交通中电动垂直起降飞行器的轨迹设计问题，提出整合多方面数据和约束的混合框架，能在考虑多达250个建筑物障碍物等情况下设计出安全且能减少出行时间的四维轨迹，相比不考虑约束可减少20%飞行时间估计。
中文摘要 AI 辅助
城市空中交通（UAM）借助电动垂直起降（eVTOL）飞行器有助于缓解地面交通拥堵。设计安全且能减少出行时间的eVTOL轨迹是UAM应用的关键。现有轨迹设计工作要么未充分纳入城市环境中的密集障碍物、复杂的eVTOL飞行动力学或一个或多个飞行阶段。不考虑这些因素会导致轨迹质量低甚至不可行。我们开发了一个混合框架，可整合建筑物障碍物数据、风数据、eVTOL飞行动力学和其他实际运行约束，以估计上升、巡航和下降阶段的四维eVTOL飞行轨迹，目标是最小化出行时间。该框架先以相交凸多边形填充无障碍物区域，再用基于凸集图的路径规划器识别潜在低出行时间的多边形候选序列，最后用最优控制程序给出通过之前确定序列中多边形的最终轨迹。我们在纽约市的路线上评估了该框架。在存在多达250个建筑物障碍物的情况下，我们的框架能设计出符合上述约束的轨迹。结果表明，不包含上述约束会使飞行时间估计低达20%。
英文摘要
Urban Air Mobility (UAM) with electric Vertical TakeOff and Landing (eVTOL) vehicles can help address ground traffic congestion. The design of an eVTOL trajectory that is safe and reduces travel time is key for UAM adoption. Existing works on trajectory design either may not adequately incorporate dense obstacles in urban environments, complex eVTOL flight dynamics, or one or more flight phases. Not considering these factors can result in low-quality, or worse infeasible, trajectories. We develop a hybrid framework that can integrate building obstacles data, wind data, eVTOL flight dynamics, and other real-world operational constraints to estimate a four-dimensional eVTOL flight trajectory in ascent, cruise, and descent that aims to minimize travel time. Our framework first fills the obstacle-free regions with intersecting convex polygons, then identifies potentially low-travel time candidate sequences of these polygons using a Graph of Convex Sets-based path planner, and then uses an Optimal Control Program to give the final trajectory that passes through the polygons in a sequence identified before. We evaluate our framework on routes within New York City. Our framework can design trajectories respecting the above constraints in the presence of as many as 250 building obstacles. We show that not including the above constraints can underestimate the flight time by as much as 20\%.
