Control Synthesis and Visual Perception for Agile Autonomous Vehicles

Speaker: Sertac Karaman
Associate Professor of Aeronautics and Astronautics
Laboratory for Information and Decision Systems
Institute for Data, Systems and Society
Massachusetts Institute of Technology

Time: 27 April 17, Thursday, 3pm
Place: ISEC 136

Abstract:
Agile autonomous vehicles that can exploit the full envelope of their dynamics to navigate through complex environments at high speeds require fast, accurate perception and control algorithms. In the first part of the talk, we focus on the control synthesis problems for agile vehicles. We present computationally-efficient algorithms for automated controller synthesis for systems with high-dimensional state spaces. In a nutshell, the new algorithms represent the value function in a compressed form enabled by a novel compression technique called the function train decomposition; and compute the controller using dynamic programming techniques while keeping the value function in this compressed format. We show that the new algorithms have run times that scales polynomially with the dimensionality of the state space and the rank of the value of the value function. In computational experiments, the new algorithms provide up to ten orders of magnitude improvement, when compared to standard dynamic programming algorithms, such as value iteration. In the second part of the talk, we focus on perception problems. We present new visual-inertial navigation algorithms that carefully select features to maximize the localization performance. The resulting algorithms are based on sub-modular optimization techniques, which lead to efficient algorithms with performance guarantees.

Bio: Sertac Karaman is the Class of ’48 Career Development Chair 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 IEEE Robotics and Automation Society Early Career Award in 2017, an Office of Naval Research Young Investigator Award in 2017, 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.