In recent years, robot motion planning algorithms have been developed to solve complex motion planning problems.  However, these algorithms are typically designed for the point-to-point planning objective.  This talk will consider path planning for more complex objectives.  Such objectives can be given in a high-level language, where logical and temporal constraints on the robot path can be specified.  A method will be presented to compute robot controllers that optimize motion while satisfying the planning objective.  Extensions of this method to nondeterministic environments and unreliable robots will also be discussed.  Finally, the connection will be made between this work and persistent monitoring tasks, where robots must continually survey a changing environment.  

Bio: Stephen L. Smith received the B.Sc. degree from Queen‚Äôs University, Canada in 2003, the M.A.Sc. degree from the University of Toronto, Canada in 2005, and the Ph.D.  degree from the University of California, Santa Barbara in 2009.  From 2009 to 2011 he was a postdoctoral researcher with the Computer Science & Artificial Intelligence Lab at MIT.  He is currently an assistant professor in Electrical and Computer Engineering at the University of Waterloo, Canada.  His main research interests lie in the control and optimization of autonomous systems, with a particular emphasis on robotic motion planning and coordination.

Host: Francesco Bullo