RAL 2022: Model-Free Safety-Critical Control for Robotic Systems

Tamas G. Molnar, Ryan K. Cosner, Andrew W. Singletary, Wyatt Ubellacker, Aaron D. Ames. [pdf]

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.

This is work performed in collaboration with Tamas Molnar, Andrew Singletary, Wyatt Ubellacker, and Aaron Ames. It was originally submitted to the IEEE RAL Journal.

The extended publication can be found here (https://arxiv.org/pdf/2109.09047.pdf).