# Decentralized traffic management of autonomous drones

arXiv: https://arxiv.org/abs/2312.11207 · HTML: https://mlsanity.com/paper/2312.11207/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments](https://mlsanity.com/paper/2011.04183/) (2020)
- [Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning](https://mlsanity.com/paper/1909.05150/) (2019)
- [When slower is faster](https://mlsanity.com/paper/1506.06796/) (2015)
- [Scalability in Computing and Robotics](https://mlsanity.com/paper/2006.04969/) (2020)
- [Guiding the Self-organization of Cyber-Physical Systems](https://mlsanity.com/paper/1912.00758/) (2019)
- [Sky Highway Design for Dense Traffic](https://mlsanity.com/paper/2010.09159/) (2020)

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

- [Watching Swarm Dynamics from Above: A Framework for Advanced Object Tracking in Drone Videos](https://mlsanity.com/paper/2406.07680/) (2024)
- [Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space](https://mlsanity.com/paper/2408.07395/) (2024)

## Abstract (arXiv)

Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task - filled with conflicts - is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a circular area with a radius of 125 meters.