Control of High-performance Autonomous Vehicle and Their Systems
Date: Tuesday, November 08, 2016
Time: 11:00 AM to 12:00 PM Note: all times are in the Eastern Time Zone
Location: 32-G449 Patil/Kiva
Host: Russ Tedrake
Contact: Nick Roy, firstname.lastname@example.org
Speaker URL: None
TALK: Control of High-performance Autonomous Vehicle and Their Systems
Abstract: In this talk, we present control and planning algorithms for autonomous vehicles that deliver high performance. In the first part of the talk, we focus on control problems for vehicle-level autonomy, i.e., for controlling a single vehicle with complex dynamics to execute complex tasks. Specifically, we introduce novel algorithms that construct arbitrarily good solutions for stochastic optimal control problems. We show that their running time scales linearly with dimension and polynomially with the rank of the optimal cost-to-go function, breaking the curse of dimensionality for low-rank problems. Our results are enabled by a novel continuous analogue of the well-known tensor-train decomposition. We demonstrate the new algorithms on a simulated perching problem, where the computational savings reach ten orders of magnitude when compared to naive approaches, such as value iteration on a grid. In the second part of the talk, we focus on system-level autonomy, i.e., problems that concern systems that consist of several autonomous vehicles. Specifically, we present results on optimal coordination of vehicles passing through an intersection. We reduce the problem to a polling system, under mild technical conditions. We show that the resulting system provides orders of magnitude improvement in delay, when compared to conventional traffic light systems.
Bio: Sertac Karaman is an Associate Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He has obtained B.S. degrees in mechanical engineering and in computer engineering from the Istanbul Technical University, Turkey, in 2007; an S.M. degree in mechanical engineering from MIT in 2009; and a Ph.D. degree in electrical engineering and computer science also from MIT in 2012. His research interests lie in the broad areas of robotics and control theory. In particular, he studies the applications of probability theory, stochastic processes, stochastic geometry, formal methods, and optimization for the design and analysis of high-performance cyber-physical systems. The application areas of his research include driverless cars, unmanned aerial vehicles, distributed aerial surveillance systems, air traffic control, certification and verification of control systems software, and many others. He is the recipient of an Army Research Office Young Investigator Award in 2015, National Science Foundation Faculty Career Development (CAREER) Award in 2014, AIAA Wright Brothers Graduate Award in 2012, and an NVIDIA Fellowship in 2011.
Created by Nick Roy at Monday, November 07, 2016 at 2:41 PM.