Safe Control using Vision-based Control Barrier Function (V-CBF)

Abstract

Safe motion control in unknown environments is one of the challenging tasks in robotics, such as autonomous navigation. Control Barrier Function (CBF), as a strong math-ematical tool, has been widely used in many safety-critical systems to satisfy safety requirements. However, there are only a handful of recent studies on safety controllers with perception inputs. Common assumptions in most of the works are that the CBF is already known and obstacles have predefined shapes. In this work, we introduce a novel Vision-based Control Barrier Function (V-CBF), which enables generalization to new environments and obstacles of arbitrary shapes. We then derive CBF safety conditions over RGB-D space and relate those to actual robot control inputs. To train the CBF function, we introduce a method to generate ground truth with desired properties complying with CBF and a method to generate part of the CBF as an image-to-image translation problem. We finally demonstrate the efficacy of V-CBF on the safe control of an autonomous car in CARLA simulator.