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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2018.2865891