AttentionSwarm: 動的環境におけるCrazyflieドローンのための注意制御バリア関数を用いた強化学習
AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments
注意機構と制御バリア関数を組み合わせ、動的なドローン競争環境で衝突回避と軌道最適化を実現する群制御手法を提案し、Crazyflie 2.1で95-100%の衝突回避率を達成した。
著者: Grik Tadevosyan, Valerii Serpiva, Aleksey Fedoseev, Roohan Ahmed Khan, Demetros Aschu, Faryal Batool, Nickolay Efanov, Artem Mikhaylov, Dzmitry Tsetserukou
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.
