AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
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.
