Abstract

A lot of progress has been made in the development of computational algorithms and software tools for optimization-based motion planning and control of (semi-)autonomous systems. There exist many efficient convex quadratic programming (QP) algorithms for model predictive control (MPC) of linear or linearized systems, as well as sequential convex programming (SCP) algorithms for MPC of smooth nonlinear systems. Motivated by these successes, a relatively new trend in the control community relates to the development and application of mixed-integer programming (MIP) for real-time motion planning and decision making, including both continuous and discrete variables. In this talk, I present some recent work on a tailored branch-and-bound method for real-time motion planning and decision making on embedded processing units. In addition, I will discuss two applications related to automated driving and traffic control.

Bio

Rien Quirynen received the Bachelor’s degree in computer science and electrical engineering and the Master’s degree in mathematical engineering from KU Leuven, Belgium. He received a four-year Ph.D. Scholarship from the Research Foundation–Flanders (FWO) in 2012-2016, and the joint Ph.D. degree from KU Leuven, Belgium and the University of Freiburg, Germany. Since the start of 2017, Dr. Quirynen joined Mitsubishi Electric Research Laboratories (MERL) in Cambridge, MA, USA, where he is currently a principal research scientist. His research focuses mainly on numerical optimization algorithms for decision making, motion planning and control of autonomous systems.