Foehn Philipp, Romero Angel, Scaramuzza Davide
Robotics and Perception Group, University of Zurich, Zurich, Switzerland.
Sci Robot. 2021 Jul 21;6(56). doi: 10.1126/scirobotics.abh1221.
Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor's actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world's largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.
四旋翼飞行器是最灵活的飞行机器人之一。然而，在致动极限下通过多个航路点规划时间最优轨迹仍然是一个悬而未决的问题。这对于诸如检查、递送、搜索和救援以及无人机竞赛等应用至关重要。早期的工作使用多项式轨迹公式，由于其固有的平滑性，这些公式没有充分发挥执行器的潜力。最近的工作采用了数值优化，但要求在特定离散时间将航路点作为成本或约束进行分配。然而，这种时间分配是先验未知的，使得先前的工作无法产生真正的时间最优轨迹。为了生成真正的时间最优轨迹，我们提出了一种时间分配问题的解决方案，同时充分利用四旋翼飞行器的执行器潜力。我们通过引入沿轨迹的进度公式来实现这一点，该公式能够同时优化时间分配和轨迹本身。我们将我们的方法与相关方法进行比较，并在世界上最大的运动捕捉系统之一的实际飞行中对其进行验证，在无人机竞赛任务中我们的表现优于人类专家无人机飞行员。
