Aggressive driving with model predictive path integral control
| Autor: | Paul Drews, James M. Rehg, Brian Goldfain, Evangelos A. Theodorou, Grady Williams |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Stochastic control
0209 industrial biotechnology Mathematical optimization Kullback–Leibler divergence Computer science 02 engineering and technology Aggressive driving Model predictive control 020901 industrial engineering & automation Control theory Path integral formulation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Importance sampling Energy (signal processing) |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra.2016.7487277 |
| Popis: | In this paper we present a model predictive control algorithm designed for optimizing non-linear systems subject to complex cost criteria. The algorithm is based on a stochastic optimal control framework using a fundamental relationship between the information theoretic notions of free energy and relative entropy. The optimal controls in this setting take the form of a path integral, which we approximate using an efficient importance sampling scheme. We experimentally verify the algorithm by implementing it on a Graphics Processing Unit (GPU) and apply it to the problem of controlling a fifth-scale Auto-Rally vehicle in an aggressive driving task. |
| Databáze: | OpenAIRE |
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