通过顺序路径规划实现可证明安全和稳健的无人机路由：以旧金山和湾区为例
Provably Safe and Robust Drone Routing via Sequential Path Planning: A Case Study in San Francisco and the Bay Area
AI总结：
本文通过顺序路径规划方法在大规模多无人机系统中实现安全高效的路径规划，展示了其在城市和多城市环境中的应用效果。
摘要：
可证明安全和可扩展的多车辆路径规划是一个重要且紧迫的问题，由于未来民用空域自动化水平的提高。哈密顿-雅可比（HJ）可达性是分析此类安全关键系统的理想工具，并已成功应用于几个小规模问题。然而，直接将HJ可达性应用于大规模系统通常是不可行的，因为其计算复杂性随系统维度呈指数级增长，也称为“维度诅咒”。为克服这一问题，之前提出了顺序路径规划（SPP）方法，该方法为车辆分配严格优先级；SPP允许多车辆路径规划以线性增长的计算复杂性进行。在本工作中，我们展示了SPP算法在大规模系统中的潜力。特别是，我们在两种不同的城市环境中模拟了大规模多车辆系统，即城市环境和多城市环境，并使用SPP算法进行轨迹规划。SPP能够在存在车辆动态扰动的情况下，高效地设计无碰撞轨迹。为确保车辆安全到达目的地，我们的方法自动为车辆分配空间时间预留，同时考虑风等扰动幅度的可证明安全方式。我们的仿真结果表明，空中存在直观的多车道结构，其中车道数量和车道之间的距离取决于扰动幅度和其他问题参数。
英文摘要：
Provably safe and scalable multi-vehicle path planning is an important and urgent problem due to the expected increase of automation in civilian airspace in the near future. Hamilton-Jacobi (HJ) reachability is an ideal tool for analyzing such safety-critical systems and has been successfully applied to several small-scale problems. However, a direct application of HJ reachability to large scale systems is often intractable because of its exponentially-scaling computation complexity with respect to system dimension, also known as the "curse of dimensionality". To overcome this problem, the sequential path planning (SPP) method, which assigns strict priorities to vehicles, was previously proposed; SPP allows multi-vehicle path planning to be done with a linearly-scaling computation complexity. In this work, we demonstrate the potential of SPP algorithm for large-scale systems. In particular, we simulate large-scale multi-vehicle systems in two different urban environments, a city environment and a multi-city environment, and use the SPP algorithm for trajectory planning. SPP is able to efficiently design collision-free trajectories in both environments despite the presence of disturbances in vehicles' dynamics. To ensure a safe transition of vehicles to their destinations, our method automatically allocates space-time reservations to vehicles while accounting for the magnitude of disturbances such as wind in a provably safe way. Our simulation results show an intuitive multi-lane structure in airspace, where the number of lanes and the distance between the lanes depend on the size of disturbances and other problem parameters.
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