Computer Science Department
School of Computer Science, Carnegie Mellon University


Probabilistic Algorithms in Robotics

Sebastian Thrun

April 2000

Keywords: Artificial intelligence, Bayes filters, decision theory, robotics, localization, machine learning, mapping, navigation, particle filters, planning, POMDPs, position estimation

This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progress in the field, using in-depth examples to illustrate some of the nuts and bolts of the basic approach. Our central conjecture is that the probabilistic approach to robotics scales better to complex real-world applications than approaches that ignore a robot's uncertainty.

20 pages

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