Speaker
Professor
Marco
Pavone
Stanford
University
Title
Fast Marching Trees: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions
Abstract
Motion planning is a fundamental problem in the field of robotics, with applications as diverse as robotic manufacturing, autonomous driving, spacecraft control, and molecular simulation. In this talk, I will present and discuss a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT *), which is asymptotically optimal, appears to converge to an optimal solution faster (and sometimes significantly faster) than its state-of-the-art counterparts, such as PRM* and RRT*, and enables the inclusion in the planning process of integro-differential constraints. The FMT * algorithm essentially performs a "lazy" dynamic programming recursion on a set of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-come space. As such, this algorithm is conceptually related to the Fast Marching Method for the solution of eikonal equations. The optimality result is proven with a novel analysis technique that provides convergence rate bounds - a first in this field. I will conclude the talk by discussing the application of the FMT* algorithm to the "spacecraft motion planning problem," which presents unique aspects with respect to its robotic counterpart and whose solution would be a key enabler for several future NASA missions.
Speaker's biography
Dr. Marco Pavone is an Assistant Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and holds courtesy appointments in the Department of Electrical Engineering, in the Institute for Computational and Mathematical Engineering, and in the Information Systems Laboratory. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. Dr. Pavone’s areas of expertise lie in the fields of controls and robotics. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on large-scale robotic networks and autonomous aerospace vehicles. Dr. Pavone is a recipient of a NASA Early Career Faculty award, a Hellman Faculty Scholar Award, and was named NASA NIAC Fellow in 2011.
Invited by Professor Stephen L. Smith