# SafeSwarm: Decentralized Safe RL for the Swarm of Drones Landing in Dense Crowds

arXiv: https://arxiv.org/abs/2501.07566 · HTML: https://mlsanity.com/paper/2501.07566/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 13 of 13)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics](https://mlsanity.com/paper/2109.06325/) (2021)
- [Learning Speed Adaptation for Flight in Clutter](https://mlsanity.com/paper/2403.04586/) (2024)
- [Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach](https://mlsanity.com/paper/2105.00657/) (2021)
- [MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints](https://mlsanity.com/paper/2403.17551/) (2024)
- [Safe Navigation and Obstacle Avoidance Using Differentiable Optimization Based Control Barrier Functions](https://mlsanity.com/paper/2304.08586/) (2023)
- [Distributionally Robust Differential Dynamic Programming with Wasserstein Distance](https://mlsanity.com/paper/2305.09760/) (2023)
- [TornadoDrone: Bio-inspired DRL-based Drone Landing on 6D Platform with Wind Force Disturbances](https://mlsanity.com/paper/2406.16164/) (2024)
- [Aperiodic Communication for MPC in Autonomous Cooperative Landing](https://mlsanity.com/paper/2102.07403/) (2021)
- [Vision-driven UAV River Following: Benchmarking with Safe Reinforcement Learning](https://mlsanity.com/paper/2409.08511/) (2024)
- [MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV](https://mlsanity.com/paper/2307.14147/) (2023)
- [ABNet: Attention BarrierNet for Safe and Scalable Robot Learning](https://mlsanity.com/paper/2406.13025/) (2024)

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

- [AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments](https://mlsanity.com/paper/2503.07376/) (2025)
- [Distributed Direct Preference Optimization](https://mlsanity.com/paper/2605.20696/) (2026)

## Abstract (arXiv)

This paper introduces a safe swarm of drones capable of performing landings in crowded environments robustly by relying on Reinforcement Learning techniques combined with Safe Learning. The developed system allows us to teach the swarm of drones with different dynamics to land on moving landing pads in an environment while avoiding collisions with obstacles and between agents. The safe barrier net algorithm was developed and evaluated using a swarm of Crazyflie 2.1 micro quadrotors, which were tested indoors with the Vicon motion capture system to ensure precise localization and control. Experimental results show that our system achieves landing accuracy of 2.25 cm with a mean time of 17 s and collision-free landings, underscoring its effectiveness and robustness in real-world scenarios. This work offers a promising foundation for applications in environments where safety and precision are paramount.