用控制屏障函数保障移动机器人网络强r-鲁棒性，无需固定拓扑。
Maintaining Strong r-Robustness in Reconfigurable Multi-Robot Networks using Control Barrier Functions
- 设计基于CBF的动态约束，实时维持通信图强r-鲁棒性
- 在狭窄环境导航中仍保持至少3个可靠连接节点
- 适合需灵活组网的多机器人协同任务
在领导者-跟随者一致性协议中，通信图的强r-鲁棒性是确保跟随者在存在异常节点时仍能达成一致的充分条件。以往研究假设机器人可形成或切换至预设的、已知鲁棒性的网络拓扑，但在基于距离的通信模型下，机器人在通过狭窄走廊等空间受限环境时可能无法实现这些拓扑。本文提出一种控制屏障函数（CBF），可在不维持任何固定拓扑的前提下，确保机器人通信图的强r-鲁棒性始终高于某一阈值。该方法直接优化鲁棒性指标，使机器人能在完成任务的同时保持灵活可重构的网络结构。通过多种仿真与硬件实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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