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

TL;DR

This paper introduces a robust multi-drone trajectory planning framework that efficiently computes collision-free, high-speed paths in large scenes, incorporating safety constraints and drone dynamics for hundreds of drones in minutes.

Contribution

It presents a novel planning framework using a free-space map and capsule-like safety constraints, with a new flatness-based optimization approach considering nonlinear drag effects.

Findings

Successfully plans trajectories for hundreds of drones in large scenes

Achieves collision-free, high-speed trajectories within minutes

Validated robustness and effectiveness through large-scale simulations

Abstract

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.