The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

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.

Keywords

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

Notes

Associated Lab(s) / Group(s): Robot Learning Lab

Number of pages: 20

Text Reference

Sebastian Thrun, "Probabilistic Algorithms in Robotics," tech. report CMU-CS-00-126, Computer Science Department, Carnegie Mellon University, April, 2000

BibTeX Reference

@techreport{Thrun_2000_3353,
   author = "Sebastian Thrun",
   title = "Probabilistic Algorithms in Robotics",
   booktitle = "",
   institution = "Computer Science Department",
   month = "April",
   year = "2000",
   number= "CMU-CS-00-126",
   address= "Pittsburgh, PA",
}

The Robotics Institute

Menu