# Robust Trajectory Planning for Spatial-Temporal Multi-Drone Coordination in Large Scenes

arXiv: https://arxiv.org/abs/2109.08403 · HTML: https://mlsanity.com/paper/2109.08403/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2021.

## Built on by (most cited first, 2 of 2)

- [Automatic Generation of Aerobatic Flight in Complex Environments via Diffusion Models](https://mlsanity.com/paper/2504.15138/) (2025)
- [PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner](https://mlsanity.com/paper/2406.10060/) (2024)

## Abstract (arXiv)

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.