April 18, 2019, ESB2001

The talk addresses navigation in an environment occupied by other interacting agents (e.g. vehicles, robots, pedestrians, pods) that cannot communicate to one another. In contrast to the path planning problem, the difficulty here is that agents cooperate and compete, creating feedback loops each only partially controls. The talk starts with a very high level overview of two control design methods: Model Predictive Control (MPC) and Control Barrier Functions (CBF). The MPC is powerful, yet computationally expensive. The CBF approach is computationally simpler and handles non-convex constraints gracefully, while, being recently introduced, is only partially understood. Performance of a CBF controller is illustrated with a real industrial robot avoiding a stationary cone.

Dr. Mrdjan Jankovic received his doctoral degree from Washington University, St. Louis in 1992. He held postdoctoral positions with Washington University and UC Santa Barbara before joining Ford in 1995. He is currently a Senior Technical Leader at Ford Research, working on development of control technologies for powertrain and driver assist applications. Dr. Jankovic coauthored one book, four book chapters, and more than 100 technical papers. He is a co-inventor on 80 US patents, 20 of which are used in Ford products. He received AACC Control Engineering Practice Award, IEEE Control Systems Technology Award, Ford’s most prestigious Dr. Haren Gandhi Research and Innovation Award, and best paper awards from IEEE, SAE, and AVEC. Dr. Jankovic is a Fellow of the IEEE.