用注意力机制提升无人机群动态避障能力，实测零碰撞率。
AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments
- 结合注意力机制与安全约束函数，动态识别关键障碍物。
- 在动态环境中实现95%~100%的无碰撞导航成功率。
- 适合高速多机协同场景，如物流巡检与竞速飞行。
我们提出AttentionSwarm，一个用于评估动态无人机竞速场景中安全高效群体控制的新基准。核心是基于注意力机制的控制屏障函数（CBF）框架，将注意力机制与安全控制理论融合，实现实时避障与轨迹优化。该框架通过注意力权重动态优先处理近场关键障碍物与集群成员，同时利用CBF形式化保证安全，强制执行无碰撞约束。AttentionSwarm算法在由Crazyflie 2.1微型四旋翼组成的集群上开发并测试，采用Vicon运动捕捉系统实现室内高精度定位与控制。实验结果表明，系统在动态多智能体无人机竞速环境中实现了95%至100%的无碰撞导航率，充分验证了其在真实场景中的有效性与鲁棒性。本工作为物流、巡检与竞速等高速多机器人应用提供了可靠基础。
原文摘要 · Abstract (English)
We introduce AttentionSwarm, a novel benchmark designed to evaluate safe and efficient swarm control in 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 swarm's vicinity using attention weights, while CBFs formally guarantee safety by enforcing collision-free constraints. The AttentionSwarm 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 a 95-100% collision-free navigation rate in a dynamic multi-agent drone racing environment, underscoring its effectiveness and robustness in real-world scenarios. This work offers a promising foundation for safe, high-speed multi-robot applications in logistics, inspection, and racing.
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