Model-Free Safety-Critical Control for Robotic Systems

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.

Accepted to the IEEE Robotics and Automation Letters (RA-L) and submitted to the 2022 IEEE International Conference on Robotics and Automation (ICRA). 8 pages, 5 figures

References in corpus (3)

• Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
• Toward Safety-Aware Informative Motion Planning for Legged Robots
• Model Free Barrier Functions via Implicit Evading Maneuvers

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• Disturbance Observers for Robust Safety-critical Control with Control Barrier Functions
• Safety-Critical Traffic Control by Connected Automated Vehicles
• Safe Reinforcement Learning Using Black-Box Reachability Analysis
• Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization