Control Barrier Corridors: From Safety Functions to Safe Sets

2026-03-06

AI Summary

This work addresses the challenge of ensuring safe and autonomous robot navigation in complex dynamic environments by proposing a novel “Control Barrier Corridor” framework. It unifies control barrier functions with safety corridors for the first time, reformulating safety constraints as locally feasible target regions. By integrating feedback control with convex optimization, the method generates reference trajectories that guarantee continuous safety in real time. The approach is validated on fully actuated systems, unicycle models, and linear output regulation systems, demonstrating its broad applicability. A key contribution lies in establishing a tunable trade-off between safety and responsiveness, enabling verifiably safe, persistent, and adaptive exploration even in unknown environments.

Technology Category

Natural Language Processing: Safety and RobustnessIntelligent Robots: Behavior Learning & ControlPlanning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySystems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systemsSecurity and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• safe autonomy
• control barrier functions
• safe motion corridors
• safety verification
• autonomous exploration

Innovation

• Control Barrier Corridors
• Control Barrier Functions
• Safe Motion Planning
• Autonomous Exploration
• Safety-Reactiveness Trade-off