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Motion Planning with Probabilistic Roadmaps

Jean-Claude Latombe
Stanford University

Monday, April 11, 2005
CII 4050 - 4:00 p.m. to 5:00 p.m.
Refreshments at 3:30 p.m.

For over 15 years, a major research theme in my group has been the development of random sampling schemes to create efficient motion planners. The main outcome of this research has been the Probabilistic Roadmap approach (PRM) to motion planning. Originally, this approach was intended to compute collision-free paths of robots with "many" degrees of freedom - at that time, 4 or more. But, over the years, successive improvements (as well as faster computers) made it possible to handle robotic systems with several dozen degrees of freedom operating in complex geometric environments. PRM was also extended to solve planning problems with motion constraints other than collision avoidance, for instance, visibility, equilibrium, contact, and kinodynamic constraints.

Concurrently, PRM has also been applied to non-robotics applications, e.g., for animating autonomous digital characters, designing product that can easily be assembled and serviced, testing whether architectural designs satisfy building codes, providing interactive tools to navigate in huge virtual reality models, planning complex surgical operations, and studying folding and binding molecular motions. This lecture will consist of three parts. First, I will review the PRM approach and various underlying techniques, especially sampling strategies. Then, I will discuss the probabilistic foundations of the approach and related theoretical results. In particular, I will argue that the main outcome of PRM is what its success tells us about motion planning problems, rather than the approach itself. Finally, I will discuss the recent application of PRM to legged robots navigating on steep irregular terrain - more specifically, rock-climbing robots. This application, which requires processing several thousand planning queries, many of which are not feasible, raises new issues associated with the fact that PRM is only probabilistically complete.

This lecture is based on the work of many students, including Jerome Barraquand, Tsai-Yen Li, Yotto Koga, Lydia Kavraki, Rhea Tombropoulos, Amit Singh, David Hsu, James Kuffner, Gildardo Sanchez, Mitul Saha, Tim Bretl, and Kris Hauser.

Short Bio: Jean-Claude Latombe is the Kumagai Professor of Computer Science at Stanford University. He received his PhD from the National Polytechnic Institute of Grenoble (INPG) in 1977. He was on the faculty of INPG from 1980 to 1984, then he joined ITMI (Industry and Technology for Machine Intelligence), a company that he had co-founded in 1982. He moved to Stanford in 1987. At Stanford, he served as the Chairman of the Computer Science Department from 1997 till 2001, and on the BioX Leadership Council from 2002 till 2004. His main research interests are in Artificial Intelligence, Robotics, Computational Biology, Computer-Aided Surgery, and Graphic Animation.
URL: http://ai.stanford.edu/~latombe

Last updated: March 2, 2005