27 citations · 56 across the 20 of their papers we have counts for
5 papers · 1 filter
Learning Safe Control for Multi-Robot Systems: Methods, Verification, and Open Challenges
Kunal Garg, Songyuan Zhang, Oswin So +2
In this survey, we review the recent advances in control design methods for robotic multi-agent systems (MAS), focussing on learning-based methods with safety considerations. We st…
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help
Charles Dawson, Chuchu Fan
To ensure safe, reliable operation of the electrical grid, we must be able to predict and mitigate likely failures. This need motivates the classic security-constrained AC optimal…
A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling
Charles Dawson, Chuchu Fan
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of…
Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions
Ji Yin, Charles Dawson, Chuchu Fan +1
Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesiz…
Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments
Charles Dawson, Ashkan Jasour, Andreas Hofmann +1
Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly im…