# Downwash-Aware Trajectory Planning for Large Quadrotor Teams

arXiv: https://arxiv.org/abs/1704.04852 · HTML: https://mlsanity.com/paper/1704.04852/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2017.

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

- [Graph-Based Multi-Robot Path Finding and Planning](https://mlsanity.com/paper/2206.11319/) (2022)
- [SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography](https://mlsanity.com/paper/2412.08428/) (2024)

## Abstract (arXiv)

We describe a method for formation-change trajectory planning for large quadrotor teams in obstacle-rich environments. Our method decomposes the planning problem into two stages: a discrete planner operating on a graph representation of the workspace, and a continuous refinement that converts the non-smooth graph plan into a set of C^k-continuous trajectories, locally optimizing an integral-squared-derivative cost. We account for the downwash effect, allowing safe flight in dense formations. We demonstrate the computational efficiency in simulation with up to 200 robots and the physical plausibility with an experiment with 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.