Probabilistic Algorithms in Robotics

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


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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",
}