mlsanityData to 14 Oct 2026

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

arXiv 2501.07566 ↗First posted 13 Jan 2025cs.ROcited by 2

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

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.

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.

Builds on

13 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Vision-driven UAV River Following: Benchmarking with Safe Reinforcement Learning13 Sep

Other references · 12

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 76
  3. 2021safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Roboticscited by 50
  4. 2024Learning Speed Adaptation for Flight in Cluttercited by 14
  5. 2021Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approachcited by 7
  6. 2024MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraintscited by 7
  7. 2023Safe Navigation and Obstacle Avoidance Using Differentiable Optimization Based Control Barrier Functionscited by 5
  8. 2023Distributionally Robust Differential Dynamic Programming with Wasserstein Distancecited by 3
  9. 2024TornadoDrone: Bio-inspired DRL-based Drone Landing on 6D Platform with Wind Force Disturbancescited by 3
  10. 2021Aperiodic Communication for MPC in Autonomous Cooperative Landingcited by 2
  11. 2023MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAVcited by 1
  12. 2024ABNet: Attention BarrierNet for Safe and Scalable Robot Learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environmentscited by 0
  2. 2026Distributed Direct Preference Optimizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning
  2. 2026High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning
  3. 2024MARLander: A Local Path Planning for Drone Swarms using Multiagent Deep Reinforcement Learning ↗
  4. 2026Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
  5. 2026Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation
  6. 2024Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
  7. 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation
  8. 2026Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns