AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments

Grik Tadevosyan; Valerii Serpiva; Aleksey Fedoseev; Roohan Ahmed Khan; Demetros Aschu; Faryal Batool; Nickolay Efanov; Artem Mikhaylov; Dzmitry Tsetserukou

arXiv · 2503.07376

AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments

Grik Tadevosyan·Valerii Serpiva·Aleksey Fedoseev·Roohan Ahmed Khan·Demetros Aschu·Faryal Batool·Nickolay Efanov·Artem Mikhaylov·Dzmitry Tsetserukou

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Abstract

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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Grik Tadevosyan, Valerii Serpiva, Aleksey Fedoseev, Roohan Ahmed Khan, Demetros Aschu, Faryal Batool, Nickolay Efanov, Artem Mikhaylov, Dzmitry Tsetserukou. 2025-03-10. AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments. https://arxiv.org/abs/2503.07376

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