Model-Free Safety-Critical Control for Robotic Systems
This work addresses safety-critical control for robotic systems, offering a model-free approach that is incremental by building on control barrier function theory.
The paper tackles the problem of ensuring safety in robotic systems without requiring a detailed dynamical model, by synthesizing safe velocities using control barrier functions and tracking them with a controller, achieving theoretical safety guarantees and demonstrating application-agnostic performance in simulations and hardware experiments.
This paper presents a framework for the safety-critical control of robotic systems, when safety is defined on safe regions in the configuration space. To maintain safety, we synthesize a safe velocity based on control barrier function theory without relying on a -- potentially complicated -- high-fidelity dynamical model of the robot. Then, we track the safe velocity with a tracking controller. This culminates in model-free safety critical control. We prove theoretical safety guarantees for the proposed method. Finally, we demonstrate that this approach is application-agnostic. We execute an obstacle avoidance task with a Segway in high-fidelity simulation, as well as with a Drone and a Quadruped in hardware experiments.