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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Vydáno v:IEEE transactions on robotics Ročník 34; číslo 6; s. 1603 - 1622
Hlavní autoři: Williams, Grady, Drews, Paul, Goldfain, Brian, Rehg, James M., Theodorou, Evangelos A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2018.2865891