AI Drone faster than Humans Time Optimal Planning for Quadrotor Waypoint Flight
Quadrotors are among the most agile flying robots. However, planning timeoptimal 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 timeoptimal trajectories. To generate truly timeoptimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotors 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
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