Control Barrier Corridors: From Safety Functions to Safe Sets

TL;DR

This paper introduces control barrier corridors that unify safety functions and safe sets, enabling safe, persistent goal selection and path following in autonomous systems by extending local safety over convex barrier functions.

Contribution

It proposes a novel concept of control barrier corridors that integrate safety filtering and geometric safe zones, enhancing safe motion planning and control.

Findings

Control barrier corridors extend local safety for convex barrier functions.

Safe goal selection can be maintained over changing barrier corridors.

Demonstrated safe path following in autonomous exploration scenarios.

Abstract

Safe autonomy is a critical requirement and a key enabler for robots to operate safely in unstructured complex environments. Control barrier functions and safe motion corridors are two widely used but technically distinct safety methods, functional and geometric, respectively, for safe motion planning and control. Control barrier functions are applied to the safety filtering of control inputs to limit the decay rate of system safety, whereas safe motion corridors are geometrically constructed to define a local safe zone around the system state for use in motion optimization and reference-governor design. This paper introduces a new notion of control barrier corridors, which unifies these two approaches by converting control barrier functions into local safe goal regions for reference goal selection in feedback control systems. We show, with examples on fully actuated systems, kinematic unicycles, and linear output regulation systems, that individual state safety can be extended locally over control barrier corridors for convex barrier functions, provided the control convergence rate matches the barrier decay rate, highlighting a trade-off between safety and reactiveness. Such safe control barrier corridors enable safely reachable persistent goal selection over continuously changing barrier corridors during system motion, which we demonstrate for verifiably safe and persistent path following in autonomous exploration of unknown environments.