  In this paper, we describe a robust multi-drone planning framework for
high-speed trajectories in large scenes. It uses a free-space-oriented map to
free the optimization from cumbersome environment data. A capsule-like safety
constraint is designed to avoid reciprocal collisions when vehicles deviate
from their nominal flight progress under disturbance. We further show the
minimum-singularity differential flatness of our drone dynamics with nonlinear
drag effects involved. Leveraging the flatness map, trajectory optimization is
efficiently conducted on the flat outputs while still subject to physical
limits considering drag forces at high speeds. The robustness and effectiveness
of our framework are both validated in large-scale simulations. It can compute
collision-free trajectories satisfying high-fidelity vehicle constraints for
hundreds of drones in a few minutes.
