A Collision-Free Reactive Guidance Module for Safety-Critical Vehicles Using Local Measurements

This article addresses the problem of reactive collision avoidance for autonomous vehicles in environments with unknown dynamic obstacles. By developing a novel local measurement-based control barrier function (LM-CBF), a collision-free guidance module is established to make minimal modifications to the potentially unsafe commands at the kinematic level when encountering obstacles. Moreover, a sufficient condition is derived to ensure collision avoidance while considering the tracking performance of the underlying pre-equipped controllers, even in the presence of tracking errors. Compared with the prevailing schemes, the proposed method eliminates dependence on GPS, environmental models, and prior knowledge of obstacles, such as absolute position and velocity, and instead uses locally measurable distance and bearing information to react to obstacles for real-time avoidance. Furthermore, it offers superior plug-and-play functionality by neither altering the structure of the low-level tracking controller nor requiring a redesign of the kinematic guidance law. Rigorous theoretical analysis substantiates the safety properties of the proposed method. Numerical simulations and experiments on a physical robotic platform further validate its effectiveness and real-time performance in unknown environments with moving obstacles.