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GRAND seminar Probabilistic Motion Planning Methods and Their Applications Abstract Applications for automatic motion planning include not only robotics, but problem domains such as computer-aided design, virtual and augmented reality systems, computational biology and chemistry, animation and games. Although many deterministic motion planning methods have been proposed, most are not used in practice because they are computationally infeasible except for some restricted cases, e.g., when the robot has few degrees of freedom. Many researchers have turned their attention to randomized planners. These planners have been highly successful in solving challenging problems that were previously unsolvable and thus have become the method of choice for a wide range of applications. In this presentation, I will talk about several recently developed probabilistic motion planning methods and their applications. Biography Jyh-Ming Lien joined George Mason University as an assistant professor in January 2007. His research is in the areas of computational geometry, robotics motion planning and computer graphics. He received his B.S. in Computer Science from National ChengChi University, Taiwan, in 1999 and Ph.D. in Computer Science from Texas A&M University in 2006. He was a postdoctoral researcher at UC Berkeley before joining George Mason University. Slides
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