Safe Autonomy via Stochastic Reachability
Thursday, February 1, 2018
10:30AM – 12PM
Deploying autonomy in expensive safety-critical or high-risk systems require guarantees of safety. Two major challenges in providing these assurances are the stochasticity and the high dimensionality of the system. Stochasticity may capture human actions, disturbance effects, and mitigate the inevitable limitations in mathematical models; and high-dimensionality is inevitable as model fidelity improves. The desired guarantee may be obtained by solving the stochastic reach-avoid problem, a stochastic optimal control problem with a multiplicative cost function. Existing approaches provide approximations and suffer from the curse of dimensionality. We propose scalable algorithms to under approximate the stochastic reach-avoid probability and the associated sets using convex optimization and Fourier analysis. Our approach is grid-free and recursion-free and enables verification of high-dimensional stochastic dynamical systems. We apply our method to problems in stochastic target capture using quadrotors and satellite rendezvous and docking.
Abraham P. Vinod received the B.Tech. and the M.Tech degree in Electrical Engineering from Indian Institute of Technology, Madras in 2014. He is currently a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of New Mexico. His research interests are in the area of optimization and stochastic control, particularly stochastic reachability analysis. He was awarded Best Student Paper Award in the 2017 ACM Hybrid Systems: Computation and Control Conference for the use of Fourier transforms in stochastic reachability, the Prof. Achim Bopp Prize for Best Student Hardware Project for his M.Tech work on attitude estimation, and the Central Board of Secondary Education Merit Scholarship for his undergraduate studies.
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