Reception to follow.
Consider a robotic vehicle that is traveling in a cluttered environment or attempting to fulfill tasks that require visiting several locations. What is the maximum speed that this vehicle can achieve and maintain for a long time? How does this speed depend on the agility, perception, actuation, or computation capabilities of the vehicle? To answer these questions, we formulate various control and planning problems in stochastic obstacle fields or stochastic reward fields; subsequently, we establish novel connections between these problems and suitable fundamental problems of statistical mechanics. In particular, we point out critical phenomena, phase transitions, and universality classes in certain control and planning problems. With the help of these results, we propose efficient algorithms with provable performance guarantees.
Sertac Karaman is the Charles Stark Draper Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (since Fall 2012). He has obtained B.S. degrees in mechanical engineering and 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.