论文标题
通过测量 - 固定控制屏障功能确保学习的感知模块的安全性
Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
论文作者
论文摘要
现代非线性控制理论旨在开发反馈控制器，以赋予系统具有安全性和稳定性等属性。这些控制器确保确保的保证通常依赖于系统状态的准确估计来确定控制措施。在实践中，测量模型不确定性可能会导致降低这些保证的状态估计中的错误。在本文中，我们试图将技术从控制理论和机器学习中统一，以合成在存在测量模型不确定性的情况下实现安全的控制器。我们将测量射击控制屏障功能（MR-CBF）定义为确定安全控制输入的工具，当面对测量模型不确定性时。此外，MR-CBF用于为基于学习的感知系统的采样方法提供了信息，并量化了所得学习模型中的可忍受错误。我们证明了MR-CBF在模拟Segway系统上的测量模型不确定性实现安全性的功效。
Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model uncertainty can lead to error in state estimates that degrades these guarantees. In this paper, we seek to unify techniques from control theory and machine learning to synthesize controllers that achieve safety in the presence of measurement model uncertainty. We define the notion of a Measurement-Robust Control Barrier Function (MR-CBF) as a tool for determining safe control inputs when facing measurement model uncertainty. Furthermore, MR-CBFs are used to inform sampling methodologies for learning-based perception systems and quantify tolerable error in the resulting learned models. We demonstrate the efficacy of MR-CBFs in achieving safety with measurement model uncertainty on a simulated Segway system.
