ICAPS 04 Logo 14Th International Conference on Automated Planning & Scheduling

Whistler, British Columbia, Canada, June 3-7 2004
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Doctoral Consortium

Tutorial 4. Robot Motion Planning


Moving a robot from an initial to a finial configuration without colliding with any of the obstacles in its environment has been a core research topic in robotics over the last decade. This tutorial begins by detailing the history of motion planning punctuated by key complexity results. After presenting some of the exact approaches, motion planning techniques based on sampling are described. Sampling-based motion planners have enjoyed a great of deal of practical success and attention in the robotics community and this part of the tutorial will focus on stating the key concepts, problems and describing the state of the art.

Intended Audience

This tutorial is intended as an introduction to motion planning for AI researchers. No specific knowledge of the area will be assumed beyond basic probability theory and geometry.

Authors Information

Lydia Kavraki Lydia Kavraki is an associate professor of Computer Science and Bioengineering at Rice University. Kavraki received her B.A. from the University of Crete in Greece and her Ph.D. from Stanford University in 1995. Her research interests are in robotics, and geometric computing, physical algorithms, bioinformatics and structural computational biology. A unifying theme in her work is the investigation of algorithms and system architectures for solving complex geometric problems arising in the physical world. She is the editor of one book and the author of more than seventy refereed publications. Kavraki has received the NSF CAREER award, an Alfred P. Sloan Research Fellowship, the Association for Computing Machinery Grace Murray Hopper Award for her work on probabilistic planning, the IEEE Robotics and Automation Society Early Career Award, and was selected as one of world's top 100 young innovators by the MIT Technology Review magazine in 2002.
Andrew M. Ladd Andrew M. Ladd Ladd is currently finishing his Ph.D. at Rice University. While at Rice he has worked on localization with wireless Ethernet, analysis of Probabilistic Roadmap Methods, parallel motion planning algorithms, knot untangling with motion planning, rope simulation for knotting, deformable models for fast image segmentation and is currently working on motion planning with dynamics.

Both Lydia Kavraki and Andrew Ladd will be involved in preparing the tutorial material, while the actual presentation of that material will be given by Andrew Ladd.

Tutorial URL: http://www.cs.rice.edu/~aladd/ICAPStutorial

Tutorial 1 Tutorial 2 Tutorial 3 Tutorial 4 Tutorial 5

Last modified: Tue Jun 15 08:59:14 Eastern Daylight Time 2004