In robotics, it is often practically and theoretically convenient to design motion planners for approximate simple robot and environment models first, and then adapt such reference planners to more accurate complex settings. In this talk, I will introduce a new approach to extend the applicability of motion planners of simple settings to more complex settings using reference governors. Reference governors are add-on control schemes for closed-loop dynamical systems to enforce constraint satisfaction while maintaining stability, and offers a systematic way of separating the issues of stability and constraint enforcement. I will demonstrate example applications of reference governors for sensor-based navigation in environments cluttered with convex obstacles and for smooth extensions of low-order (e.g., position- or velocity-controlled) feedback motion planners to high-order (e.g., force/torque controlled) robot models, while retaining stability and collision avoidance properties.
Biography: Omur Arslan is currently a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he also received his Ph.D. degree in Electrical and System Engineering in August 2016. He received the B.Sc. and M.Sc. degrees in Electrical and Electronics Engineering from the Middle East Technical University, Ankara, Turkey, in 2007 and from Bilkent University, Ankara, Turkey, in 2009, respectively. His current research interests include geometric and topological characterization of clustering methods and its application to robot motion planning and control, sensor networks, machine perception, machine learning and data mining.