I. Introduction
Advances in autonomy have enabled robot application in all kinds of environments and in close interactions with humans, including autonomous navigation. In dynamic en-vironments, i.e., containing moving obstacles, the collision avoidance system must accommodate an unpredictable in-formation picture providing only a limited time to react to a collision. As a result, the effectiveness of planning-based algorithms is reduced significantly, highlighting the need for developing real-time reactive methods that ensure safety. Thus, designing controllers with formal real-time safety guarantees has become an essential aspect of such safety-critical applications and an active research area in recent years. Researchers have developed many tools to handle this problem, such as reachability analysis [1] [2] and artificial potential fields [3]. To obtain formal guaran-tees on safety (e.g., collision avoidance with obstacles), a safety critical control algorithm encompassing the trajectory tracking/planning algorithm is required that prioritizes safety over tracking. Control Barrier Functions [4] (CBFs) based approach is one such strategy in which a safe state set defined by inequality constraints is designed for the vehicle, and its quadratic programming (QP) formulation ensures forward invariance of these sets for all time.