Robust Trajectory Planning for Spatial-Temporal Multi-Drone Coordination in Large Scenes
This addresses the problem of efficient and safe coordination for large fleets of drones in complex environments, representing a strong specific gain in multi-agent robotics.
The paper tackles robust multi-drone trajectory planning in large scenes by developing a framework that uses a free-space map and capsule-like safety constraints to handle disturbances and collisions, achieving computation of collision-free trajectories for hundreds of drones in minutes.
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.