3D Robotic Swarmalators that Reconfigure, Navigate, and Avoid Obstacles
Zehui Xu, Xinyue Xu, Steven Ceron
AI summary
Problem
Prior robotic swarmalator implementations are largely confined to 2D or simulation, lacking scalable, formally safe 3D control methods that account for aerodynamic downwash and obstacle avoidance in physical drone swarms.
Approach
The authors extend the swarmalator model to 3D and integrate it with ellipsoidal Control Barrier Functions and Control Lyapunov Functions, implemented via both global and local control schemes, to guarantee formal safety and coordinated navigation for physical Crazyflie drones.
Key results
- Extended swarmalator model to 3D physical Crazyflie drone collectives
- Implemented ellipsoidal barrier functions to prevent downwash-induced instability
- Validated global and local CBF-CLF control schemes for safe obstacle navigation
- Demonstrated scalable reconfiguration and rotation through narrow passages in simulation and physical experiments
Why it matters
Provides a computationally efficient, formally safe framework for scaling 3D drone swarms in complex environments, advancing practical multi-robot coordination and deployment.
Abstract
We realize 3D robotic swarmalators that recon- figure, navigate, and avoid obstacles with formal safety on Crazyflie drones. We incorporate ellipsoidal Control Barrier Functions to avoid downwash turbulence between drones, and a combination of Control Lyapunov Function and Control Barrier Function methods to enable the collective to move toward desired locations while avoiding collisions between drones or with nearby obstacles. We implement a global control scheme that moves the collective as a single entity, and a local control scheme that enables fluid-like flow around nearby ob- stacles while maintaining the same general collective formation. Finally, we demonstrate how the swarmalator model combined with these control schemes can be used to reconfigure and rotate a drone collective so it moves through a narrow passage without colliding with the surrounding environment. Our simulations and physical experiments quantify scalability limits and validate the feasibility of implementing 3D swarmalator-based control on real drone collectives.