A Recursive CBF Framework for Safety under State Uncertainty

Abstract

The practical implementation of Control Barrier Functions (CBFs) for safety-critical control is often hindered by uncertainty in the knowledge of the state. While existing robust CBF methods address state uncertainty, they often lack recursive feasibility guarantees or fail when uncertainty levels are high, allowing the system to enter regions where no safe control input exists. To resolve this, we propose a novel framework of enforcing recursive CBFs. Rather than merely ensuring the invariance of the original safe set, this approach enforces the forward invariance of a subset of the safe region where a robustly safe control input is guaranteed to exist. This holistic framework ensures that the system never strays into ambiguous regions, providing continued feasibility and safety guarantees, regardless of the level of state uncertainty.