多无人机群在密集人群安全着陆，靠强化学习与安全机制协同实现
SafeSwarm: Decentralized Safe RL for the Swarm of Drones Landing in Dense Crowds
- 用安全屏障网络算法让不同动力学的无人机群自主避障
- 实验中着陆精度达2.25厘米，平均耗时17秒且零碰撞
- 适合对安全性要求高的真实场景，如应急救援或室内物流
本文提出一种基于强化学习与安全学习结合的多无人机群安全着陆系统，可在动态着陆平台和密集障碍物环境中实现鲁棒着陆。该系统通过安全屏障网络算法控制一群Crazyflie 2.1微型四旋翼无人机，在Vicon运动捕捉系统支持下进行室内实验，确保高精度定位与控制。实验结果表明，系统在真实场景中实现了2.25厘米的着陆精度，平均着陆时间为17秒，且所有飞行任务均无碰撞发生，验证了其有效性与鲁棒性。该研究为安全与精度要求高的实际应用提供了可行方案。
原文摘要 · Abstract (English)
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.
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