论文标题
猛禽：稳健而感知感知的轨迹重新载体快速飞行
RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight
论文作者
论文摘要
最新的轨迹重建进步使四极管能够在未知环境中自动导航。但是，高速导航仍然是一个重大挑战。在时间非常有限的情况下，现有方法在解决方案的可行性或质量上没有强大的保证。此外，大多数方法都不认为环境感知，这是快速飞行的关键瓶颈。在本文中，我们介绍了Raptor，这是一个强大而知觉的重新认识框架，以支持快速安全的飞行。设计了包含多个拓扑路径的路径引导优化（PGO）方法，以确保在非常有限的时间内找到可行和高质量的轨迹。我们还引入了一种感知感知的计划策略，以积极观察并避免未知的障碍。一种风险感知的轨迹改进可确保可以较早地观察到可能危害四极管的未知障碍，并及时避免。计划积极探索与安全导航相关的周围空间的运动。对所提出的方法进行了广泛的测试。我们将发布我们的实施方式，作为社区的开源软件包。
Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight. A path-guided optimization (PGO) approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce a perception-aware planning strategy to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively. We will release our implementation as an open-source package for the community.
