Safety is a central requirement for autonomous system operation across domains. Hamilton-Jacobi (HJ) reachability analysis can be used to construct "least-restrictive" safety filters that result in infrequent, but often extreme, control overrides. In contrast, control barrier function (CBF) methods apply smooth control corrections to guard the system against an often conservative safety boundary. This paper provides an online scheme to construct an implicit CBF through HJ reach-avoid differential dynamic programming in a receding-horizon framework, enabling smooth safety filtering with infinite-time safety guarantees. Simulations with the Dubins car and 5D bicycle dynamics demonstrate the scheme's ability to preserve safety smoothly without the conservativeness of handcrafted CBFs.

翻译：安全性是各领域自主系统运行的核心要求。Hamilton-Jacobi（HJ）可达性分析可用于构建“最小干预”的安全滤波器，该方法虽能减少干预频率，但常导致极端控制覆盖。相比之下，控制障碍函数（CBF）方法通过施加平滑的控制修正，以防止系统突破通常保守的安全边界。本文提出一种在线方案，利用滚动时域框架下的HJ可达-避免微分动态规划构建隐式CBF，从而在保证无限时间安全性的前提下实现平滑的安全滤波。基于Dubins车辆和5维自行车动力学的仿真实验表明，该方案能够在不引入人工构造CBF的保守性前提下，平滑地维持系统安全。