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.

翻译：安全自主性是机器人能够在非结构化复杂环境中安全运行的关键要求与核心赋能技术。控制屏障函数与安全运动走廊是两种广泛使用但技术路径相异的安全方法——前者为函数式方法，后者为几何式方法，均应用于安全运动规划与控制。控制屏障函数通过对控制输入进行安全滤波来限制系统安全性的衰减速率，而安全运动走廊则通过几何构造定义系统状态周围的局部安全区域，用于运动优化与参考调节器设计。本文提出控制屏障走廊的新概念，通过将控制屏障函数转化为反馈控制系统中用于参考目标选择的局部安全目标区域，实现了这两种方法的统一。我们以全驱动系统、运动学独轮车模型和线性输出调节系统为例进行论证：对于凸屏障函数，当控制收敛速率与屏障衰减速率相匹配时，单个状态的安全性可在控制屏障走廊上实现局部扩展，这揭示了安全性与响应性之间的权衡关系。此类安全控制屏障走廊能够在系统运动过程中，基于持续变化的屏障走廊实现安全可达的持续目标选择。我们通过在未知环境自主探索中实现可验证的安全持续路径跟踪，展示了该方法的有效性。