在线构建鲁棒安全过滤器，让飞行器实时应对模型误差与干扰。
Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime
- 用策略估计值函数在线生成鲁棒安全屏障函数。
- 在多种高阶约束系统上验证了安全滤波器的有效性。
- 硬件实测证明可补偿模型误差，适合无人机等实时控制场景。
控制屏障函数（CBF）在非线性系统安全控制合成中表现优异。然而，在存在扰动和输入约束的情况下，对高相对阶系统的安全性保障仍具挑战。本文提出鲁棒策略屏障函数（RPCBF），通过在线估计值函数实现鲁棒CBF近似。我们建立了该近似作为有效CBF的条件，并在多种高相对阶输入受限系统上通过仿真验证了RPCBF安全滤波器的有效性。最后，我们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，将模型误差视为扰动处理。代码与网站：www.oswinso.xyz/rpcbf/
原文摘要 · Abstract (English)
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/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。