We propose a scalable method for forward stochastic reachability analysis for uncontrolled
linear systems with affine disturbance. Our method uses Fourier transforms to efficiently …

We address the stochastic reach-avoid problem for linear systems with additive stochastic
uncertainty. We seek to compute the maximum probability that the states remain in a safe set …

We present a scalable algorithm to construct a polytopic underapproximation of the terminal
hitting time stochastic reach-avoid set, for the verification of high-dimensional stochastic LTI …

This paper considers the problem of stochastic optimal control of a Gaussian-perturbed
linear system subject to soft polytopic state constraints, hard polytopic input constraints, and …

We present a scalable underapproximation of the terminal hitting time stochastic reach-
avoid probability at a given initial condition, for verification of high-dimensional stochastic …

Probabilistic occupancy, the likelihood that the state at a known future time lies in a given
set, is important in a variety of stochastic motion planning problems. We provide efficient …

We propose a method for real-time motion planning in stochastic, dynamic environments via
a receding horizon framework that exploits computationally efficient algorithms for forward …

We propose an affine controller synthesis technique that maximizes the probability of the
state lying in a time-varying collection of safe sets for a Gaussian-perturbed linear time …

We consider stochastic optimal control of linear dynamical systems with additive non-
Gaussian disturbance. We propose a novel, sampling-free approach, based on Fourier …

As the number of small Unmanned Aircraft Systems (sUAS) in the national airspace
increases, it is becoming increasingly important to develop and implement a system for their …