Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies as a valid CBF and demonstrate the effectiveness of the RPCBF-safety filter in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of our method in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. Website including code: www.oswinso.xyz/rpcbf/

翻译：控制屏障函数（CBFs）已被证明是执行非线性系统安全控制综合的有效工具。然而，对于高相对度系统，在存在扰动和输入约束的情况下保证安全性是一个难题。本文提出鲁棒策略CBF（RPCBF），一种通过在线估计价值函数来构建鲁棒CBF近似的实用方法。我们建立了使该近似具备有效CBF资格的条件，并通过仿真在各种高相对度输入约束系统上验证了RPCBF安全过滤器的有效性。最后，我们将模型误差视为扰动，在硬件四旋翼平台上展示了该方法在补偿模型误差方面的优势。代码网站：www.oswinso.xyz/rpcbf/