The recent advancements in the field of control systems and robotics have significantly focused on enhancing safety and stability, particularly in dynamic and uncertain environments. A notable trend is the development and application of Control Barrier Functions (CBFs) to ensure system safety under various constraints. Innovations in this area include the use of zero-order CBFs for sampled-data systems, which handle safety constraints that depend on both system states and inputs without requiring differentiation operations. This approach has been shown to be effective in scenarios such as collision avoidance and rollover prevention. Another significant development is the integration of CBFs with reinforcement learning to create safety-aware algorithms capable of robust target tracking in multi-agent systems, even under input saturation and external disturbances. These CBF-Safe Reinforcement Learning (CSRL) algorithms dynamically adjust control bounds to ensure safety during evasive maneuvers. Additionally, there has been progress in modeling and ensuring numerical stability for discrete-time dynamic vehicle models, addressing critical issues like low-speed singularity, which is crucial for urban driving scenarios. These models maintain explicit forms, which are favored by model-based control algorithms, and have been validated through extensive simulations and real-world experiments.

Noteworthy papers include one that proposes a novel zero-order CBF for sampled-data systems, effectively managing high-relative degree safety constraints without differentiation. Another paper stands out for its CBF-Safe Reinforcement Learning approach, which integrates safety filters with reinforcement learning to ensure collision-free tracking in multi-agent systems.