Maintaining Strong $r$-Robustness in Reconfigurable Multi-Robot Networks using Control Barrier Functions
<|reference_start|>Maintaining Strong $r$-Robustness in Reconfigurable Multi-Robot Networks using Control Barrier Functions: In leader-follower consensus, strong $r$-robustness of the communication graph provides a sufficient condition for followers to achieve consensus in the presence of misbehaving agents. Previous studies have assumed that robots can form and/or switch between predetermined network topologies with known robustness properties. However, robots with distance-based communication models may not be able to achieve these topologies while moving through spatially constrained environments, such as narrow corridors, to complete their objectives. This paper introduces a Control Barrier Function (CBF) that ensures robots maintain strong $r$-robustness of their communication graph above a certain threshold without maintaining any fixed topologies. Our CBF directly addresses robustness, allowing robots to have flexible reconfigurable network structure while navigating to achieve their objectives. The efficacy of our method is tested through various simulation and hardware experiments.<|reference_end|>
@article{lee2024maintaining,
    title={Maintaining Strong $r$-Robustness in Reconfigurable Multi-Robot Networks
  using Control Barrier Functions},
    author={Haejoon Lee and Dimitra Panagou},
    journal={arXiv preprint arXiv:2409.14675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14675},
    primaryClass={cs.RO cs.SY eess.SY}
}