Measurements and state estimates are often imperfect in control practice, posing challenges for safety-critical applications, where safety guarantees rely on accurate state information. In the presence of estimation errors, several prior robust control barrier function (R-CBF) formulations have imposed strict conditions on the input. These methods can be overly conservative and can introduce issues such as infeasibility, high control effort, etc. This work proposes a systematic method to improve R-CBFs, and demonstrates its advantages on a tracked vehicle that navigates among multiple obstacles. A primary contribution is a new optimization-based online parameter adaptation scheme that reduces the conservativeness of existing R-CBFs. In order to reduce the complexity of the parameter optimization, we merge several safety constraints into one unified numerical CBF via Poisson's equation. We further address the dual relative degree issue that typically causes difficulty in vehicle tracking. Experimental trials demonstrate the overall performance improvement of our approach over existing formulations.

翻译：在控制实践中，测量与状态估计常存在不精确性，这对依赖精确状态信息以提供安全保障的安全关键应用构成了挑战。在存在估计误差的情况下，先前的一些鲁棒控制屏障函数（R-CBF）方法对输入施加了严格的条件。这些方法可能过于保守，并可能引入不可行性、高控制代价等问题。本文提出了一种改进R-CBF的系统性方法，并在一个于多个障碍物间导航的履带式车辆上展示了其优势。一个主要贡献是一种新的基于优化的在线参数自适应方案，该方案降低了现有R-CBF的保守性。为了降低参数优化的复杂度，我们通过泊松方程将多个安全约束合并为一个统一的数值CBF。我们进一步解决了通常导致车辆跟踪困难的双相对阶问题。实验验证表明，我们的方法相较于现有方案在整体性能上有所提升。