Maintaining Strong r-Robustness in Reconfigurable Multi-Robot Networks using Control Barrier Functions

TL;DR

This paper presents a Control Barrier Function approach that enables reconfigurable multi-robot networks to maintain strong r-robustness, ensuring consensus despite spatial constraints and without fixed topologies.

Contribution

It introduces a novel CBF-based method to preserve strong r-robustness dynamically, allowing flexible network reconfiguration in constrained environments.

Findings

Successfully maintains robustness in simulations

Enables flexible network reconfiguration during navigation

Proven effective in hardware experiments

Abstract

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.