The field of robotics is moving towards developing more safe and efficient navigation systems. Researchers are focusing on creating real-time control systems that can adapt to unknown environments and avoid obstacles. This includes the use of novel control barrier functions, model predictive control, and machine learning algorithms to ensure safe and efficient navigation. Notable papers in this area include: A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning, which provides formal guarantees for collision avoidance with road boundaries. RNBF: Real-Time RGB-D Based Neural Barrier Functions for Safe Robotic Navigation, which presents a real-time, vision-based framework for constructing continuous, first-order differentiable Signed Distance Fields of unknown environments. NMPCB: A Lightweight and Safety-Critical Motion Control Framework, which proposes a novel motion control framework that achieves a balance between real-time performance and safety.