Probabilistic Algorithms in Robotics - Robotics Institute Carnegie Mellon University

Probabilistic Algorithms in Robotics

Sebastian Thrun
Tech. Report, CMU-CS-00-126, Computer Science Department, Carnegie Mellon University, April, 2000

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.

BibTeX

@techreport{Thrun-2000-8006,
author = {Sebastian Thrun},
title = {Probabilistic Algorithms in Robotics},
year = {2000},
month = {April},
institute = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-CS-00-126},
keywords = {Artificial intelligence, Bayes filters, decision theory, robotics, localization, machine learning, mapping, navigation, particle filters, planning, POMDPs, position estimation},
}