124 citations · 346 across the 17 of their papers we have counts for
21 papers
Safety Guardrails in the Sky: Realizing Control Barrier Functions on the VISTA F-16 Jet
Andrew W. Singletary, Max H. Cohen, Tamas G. Molnar +1
The advancement of autonomous systems -- from legged robots to self-driving vehicles and aircraft -- necessitates executing increasingly high-performance and dynamic motions withou…
Safe Drone Flight with Time-Varying Backup Controllers
Andrew Singletary, Aiden Swann, Ivan Dario Jimenez Rodriguez +1
The weight, space, and power limitations of small aerial vehicles often prevent the application of modern control techniques without significant model simplifications. Moreover, hi…
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…
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…
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"…
Mixed Observable RRT: Multi-Agent Mission-Planning in Partially Observable Environments
Kasper Johansson, Ugo Rosolia, Wyatt Ubellacker +2
This paper considers centralized mission-planning for a heterogeneous multi-agent system with the aim of locating a hidden target. We propose a mixed observable setting, consisting…
Model-Free Safety-Critical Control for Robotic Systems
Tamas G. Molnar, Ryan K. Cosner, Andrew W. Singletary +2
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 synthe…
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.…
Unified Multi-Rate Control: from Low Level Actuation to High Level Planning
Ugo Rosolia, Andrew Singletary, Aaron D. Ames
In this paper we present a hierarchical multi-rate control architecture for nonlinear autonomous systems operating in partially observable environments. Control objectives are expr…
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…
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…
Formal Verification of Safety Critical Autonomous Systems via Bayesian Optimization
Prithvi Akella, Ugo Rosolia, Andrew Singletary +1
As control systems become increasingly more complex, there exists a pressing need to find systematic ways of verifying them. To address this concern, there has been significant wor…
Safety-Critical Kinematic Control of Robotic Systems
Andrew Singletary, Shishir Kolathaya, Aaron D. Ames
Over the decades, kinematic controllers have proven to be practically useful for applications like set-point and trajectory tracking in robotic systems. To this end, we formulate a…
Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays
Tamas G. Molnar, Andrew W. Singletary, Gabor Orosz +1
We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions…
Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions
Ruben Grandia, Andrew J. Taylor, Andrew Singletary +2
The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance while…
Control Barrier Functions for Sampled-Data Systems with Input Delays
Andrew Singletary, Yuxiao Chen, Aaron D. Ames
This paper considers the general problem of transitioning theoretically safe controllers to hardware. Concretely, we explore the application of control barrier functions (CBFs) to…
Barrier Functions for Multiagent-POMDPs with DTL Specifications
Mohamadreza Ahmadi, Andrew Singletary, Joel W. Burdick +1
Multi-agent partially observable Markov decision processes (MPOMDPs) provide a framework to represent heterogeneous autonomous agents subject to uncertainty and partial observation…
A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
Andrew J. Taylor, Andrew Singletary, Yisong Yue +1
In this paper we seek to quantify the ability of learning to improve safety guarantees endowed by Control Barrier Functions (CBFs). In particular, we investigate how model uncertai…
Learning for Safety-Critical Control with Control Barrier Functions
Andrew Taylor, Andrew Singletary, Yisong Yue +1
Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains. Despite this success, model…
Energy-Efficient Motion Planning for Multi-Modal Hybrid Locomotion
H. J. Terry Suh, Xiaobin Xiong, Andrew Singletary +2
Hybrid locomotion, which combines multiple modalities of locomotion within a single robot, enables robots to carry out complex tasks in diverse environments. This paper presents a…
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
Mohamadreza Ahmadi, Andrew Singletary, Joel W. Burdick +1
A multi-agent partially observable Markov decision process (MPOMDP) is a modeling paradigm used for high-level planning of heterogeneous autonomous agents subject to uncertainty an…