AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917033126395904 |
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| author | Tadevosyan, Grik Serpiva, Valerii Fedoseev, Aleksey Khan, Roohan Ahmed Aschu, Demetros Batool, Faryal Efanov, Nickolay Mikhaylov, Artem Tsetserukou, Dzmitry |
| author_facet | Tadevosyan, Grik Serpiva, Valerii Fedoseev, Aleksey Khan, Roohan Ahmed Aschu, Demetros Batool, Faryal Efanov, Nickolay Mikhaylov, Artem Tsetserukou, Dzmitry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments Tadevosyan, Grik Serpiva, Valerii Fedoseev, Aleksey Khan, Roohan Ahmed Aschu, Demetros Batool, Faryal Efanov, Nickolay Mikhaylov, Artem Tsetserukou, Dzmitry Systems and Control Robotics 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. |
| title | AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2503.07376 |