We introduce AttentionSwarm, a novel benchmark designed to evaluate safe and efficient swarm control across three challenging environments: a landing environment with obstacles, a competitive drone game setting, and a dynamic drone racing scenario. Central to our approach is the Attention Model Based Control Barrier Function (CBF) framework, which integrates attention mechanisms with safety-critical control theory to enable real-time collision avoidance and trajectory optimization. This framework dynamically prioritizes critical obstacles and agents in the swarms vicinity using attention weights, while CBFs formally guarantee safety by enforcing collision-free constraints. The safe attention 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 3.02 cm with a mean time of 23 s and collision-free landings in a dynamic landing environment, 100% and collision-free navigation in a drone game environment, and 95% and collision-free navigation for a dynamic multiagent drone racing environment, underscoring its effectiveness and robustness in real-world scenarios. This work offers a promising foundation for applications in dynamic environments where safety and fastness are paramount.

翻译：我们提出了AttentionSwarm，这是一个新颖的基准测试，旨在评估三种挑战性环境下的安全高效集群控制：包含障碍物的着陆环境、竞争性无人机游戏场景以及动态无人机竞速场景。我们方法的核心是基于注意力模型的控制屏障函数框架，该框架将注意力机制与安全关键控制理论相结合，以实现实时避碰与轨迹优化。该框架通过注意力权重动态优先处理集群附近的关键障碍物与智能体，同时CBF通过强制执行无碰撞约束，从形式上保证了安全性。安全注意力网络算法被开发出来，并利用Crazyflie 2.1微型四旋翼无人机集群进行了评估；测试在室内借助Vicon运动捕捉系统进行，以确保精确定位与控制。实验结果表明，我们的系统在动态着陆环境中实现了3.02厘米的着陆精度，平均耗时23秒，且着陆过程无碰撞；在无人机游戏环境中实现了100%无碰撞导航；在动态多智能体无人机竞速环境中实现了95%无碰撞导航，这突显了其在真实场景中的有效性与鲁棒性。此项工作为安全性与快速性至关重要的动态环境应用奠定了有前景的基础。