Hybrid dynamical systems are ubiquitous as practical robotic applications often involve both continuous states and discrete switchings. Safety is a primary concern for hybrid robotic systems. Existing safety-critical control approaches for hybrid systems are either computationally inefficient, detrimental to system performance, or limited to small-scale systems. To amend these drawbacks, in this paper, we propose a learningenabled approach to construct local Control Barrier Functions (CBFs) to guarantee the safety of a wide class of nonlinear hybrid dynamical systems. The end result is a safe neural CBFbased switching controller. Our approach is computationally efficient, minimally invasive to any reference controller, and applicable to large-scale systems. We empirically evaluate our framework and demonstrate its efficacy and flexibility through two robotic examples including a high-dimensional autonomous racing case, against other CBF-based approaches and model predictive control.

翻译：混合动力系统在机器人应用中普遍存在，因其常涉及连续状态与离散切换。安全性是混合机器人系统首要关注的问题。现有混合系统安全关键控制方法存在计算效率低、损害系统性能或仅适用于小规模系统的局限。为克服这些缺陷，本文提出一种基于学习的局部控制障碍函数（CBF）构建方法，用于保障一类非线性混合动力系统的安全性，最终得到基于神经CBF的安全切换控制器。本方法计算高效、对任意参考控制器干扰极小，且适用于大规模系统。通过两个机器人实例（包括高维自主竞速案例）的实证评估，验证了本框架相较于其他基于CBF的方法和模型预测控制的效能与灵活性。