Model-Free Safety-Critical Control for Robotic Systems

TL;DR

This paper introduces a model-free control framework for robotic safety that uses control barrier functions to ensure safety without relying on detailed robot models, demonstrated through simulations and hardware tests.

Contribution

It develops a model-free safety-critical control method using control barrier functions, applicable across various robotic platforms without high-fidelity models.

Findings

Proven safety guarantees for the control method

Successful obstacle avoidance in simulation and hardware

Application-agnostic safety control demonstrated

Abstract

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.