Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the environment becomes available. The standard approaches that ensure safety by enforcing a "stop" condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown. Moreover, the ad-hoc time and interval allocation scheme usually imposed on the trajectory also leads to conservative and slower trajectories. This work proposes FASTER (Fast and Safe Trajectory Planner) to ensure safety without sacrificing speed. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety is ensured by always having a safe back-up trajectory in the free-known space. The MIQP formulation proposed also allows the solver to choose the trajectory interval allocation. FASTER is tested extensively in simulation and in real hardware, showing flights in unknown cluttered environments with velocities up to 7.8m/s, and experiments at the maximum speed of a skid-steer ground robot (2m/s).
翻译:在未知环境中为无人驾驶航空器规划高速轨迹,需要算法技术,使快速反应时间能够随着更多关于环境的信息的出现而保证安全。通过在自由已知空间执行“停止”条件确保安全的标准方法可以严重限制车辆的速度,特别是在世界上许多未知的情况下。此外,通常在轨迹上强加的超速时间和间距分配办法也会导致保守和慢速的轨迹。这项工作提议FASTER(快、安全轨迹规划员)确保安全而无需牺牲速度。FASTER获得高速轨迹,使当地规划员能够在自由已知和未知的空间实现最佳优化。通过在自由已知空间始终有一个安全备份轨道来确保安全。MIQP的配方还允许解答器选择轨距分配。FASTER在模拟和真实硬件上进行了广泛测试,显示在未知的环境下飞行速度高达7.8米/秒,并以滑雪机地面机器人的最大速度进行实验(两米/秒)。