S. Koenig and R.G. Simmons. Xavier: A Robot Navigation Architecture Based on Partially Observable Markov Decision Process Models. In Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, D. Kortenkamp, R. Bonasso and R. Murphy (editor), 91-122. MIT Press, 1998.

Abstract: Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We present a technique for achieving this goal that uses partially observable Markov decision process models (POMDPs) to explicitly model navigation uncertainty, including actuator and sensor uncertainty and approximate knowledge of the environment. This allows the robot to maintain a probability distribution over its current pose. Thus, while the robot rarely knows exactly where it is, it always has some belief as to what its true pose is, and is never completely lost. We present a navigation architecture based on POMDPs that provides a uniform framework with an established theoretical foundation for pose estimation, path planning, robot control during navigation, and learning. Our experiments show that this architecture indeed leads to robust corridor navigation for an actual indoor mobile robot.

Warning: The downloadable version below is a preliminary version and could contain mistakes. I could no longer find the final version. If you like the preliminary version, I encourage you to buy the book.

Download the paper in pdf.

Download the paper in gzipped postscript (large download time).

Many publishers do not want authors to make their papers available electronically after the papers have been published. Please use the electronic versions provided here only if hardcopies are not yet available. If you have comments on any of these papers, please send me an email! Also, please send me your papers if we have common interests.

This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.