activity
20192022
most citedLearning for Safety-Critical Control with Control Barrier Functions

94 citations · 95 across the 6 of their papers we have counts for

collaborators

15 papers

cs.RO2022

Safety-Critical Manipulation for Collision-Free Food Preparation

Andrew Singletary, William Guffey, Tamas G. Molnar +2

Recent advances allow for the automation of food preparation in high-throughput environments, yet the successful deployment of these robots requires the planning and execution of q…

eess.SY20221 cited

Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk Control Barrier Functions

Andrew Singletary, Mohamadreza Ahmadi, Aaron D. Ames

Guaranteeing safety for robotic and autonomous systems in real-world environments is a challenging task that requires the mitigation of stochastic uncertainties. Control barrier fu…

eess.SY2022

Onboard Safety Guarantees for Racing Drones: High-speed Geofencing with Control Barrier Functions

Andrew Singletary, Aiden Swann, Yuxiao Chen +1

This paper details the theory and implementation behind practically ensuring safety of remotely piloted racing drones. We demonstrate robust and practical safety guarantees on a 7"…

eess.SY2021

Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State

Ryan K. Cosner, Andrew W. Singletary, Andrew J. Taylor +3

The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements.…

cs.RO2020

Lidar-based exploration and discretization for mobile robot planning

Yuxiao Chen, Andrew Singletary, Aaron D. Ames

In robotic applications, the control, and actuation deal with a continuous description of the system and environment, while high-level planning usually works with a discrete descri…

cs.RO2020

Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance

Andrew Singletary, Karl Klingebiel, Joseph Bourne +3

Artificial potential fields (APFs) and their variants have been a staple for collision avoidance of mobile robots and manipulators for almost 40 years. Its model-independent nature…