Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving

We present an information-theoretic approach to stochastic optimal control problems that can be used to derive general sampling-based optimization schemes. This new mathematical method is used to develop a sampling-based model predictive control algorithm. We apply this information-theoretic model p...

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Veröffentlicht in:	IEEE transactions on robotics Jg. 34; H. 6; S. 1603 - 1622
Hauptverfasser:	Williams, Grady, Drews, Paul, Goldfain, Brian, Rehg, James M., Theodorou, Evangelos A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Autonomous vehicles Control algorithms Control theory Dirt Entropy (Information theory) Information theory Mathematical models Monte Carlo methods Nonlinear control systems Optimal control parallel algorithms Parallel processing Predictive control Sampling Stochastic processes
ISSN:	1552-3098, 1941-0468
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Abstract	We present an information-theoretic approach to stochastic optimal control problems that can be used to derive general sampling-based optimization schemes. This new mathematical method is used to develop a sampling-based model predictive control algorithm. We apply this information-theoretic model predictive control scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance with a model predictive control version of the cross-entropy method.
AbstractList	We present an information-theoretic approach to stochastic optimal control problems that can be used to derive general sampling-based optimization schemes. This new mathematical method is used to develop a sampling-based model predictive control algorithm. We apply this information-theoretic model predictive control scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance with a model predictive control version of the cross-entropy method.
Author	Williams, Grady Goldfain, Brian Rehg, James M. Drews, Paul Theodorou, Evangelos A.
Author_xml	– sequence: 1 givenname: Grady orcidid: 0000-0003-1681-9224 surname: Williams fullname: Williams, Grady email: gradyrw@gatech.edu organization: School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 2 givenname: Paul orcidid: 0000-0001-8447-880X surname: Drews fullname: Drews, Paul email: pdrews3@gatech.edu organization: School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 3 givenname: Brian orcidid: 0000-0003-0754-8354 surname: Goldfain fullname: Goldfain, Brian email: bgoldfain3@gatech.edu organization: School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 4 givenname: James M. surname: Rehg fullname: Rehg, James M. email: rehg@gatech.edu organization: School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 5 givenname: Evangelos A. orcidid: 0000-0002-0834-5738 surname: Theodorou fullname: Theodorou, Evangelos A. email: evangelos.theodorou@gatech.edu organization: Daniel Guggenheim School of Aerospace Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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SubjectTerms	Autonomous vehicles Control algorithms Control theory Dirt Entropy (Information theory) Information theory Mathematical models Monte Carlo methods Nonlinear control systems Optimal control parallel algorithms Parallel processing Predictive control Sampling Stochastic processes
Title	Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving
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