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Using a genetic algorithm to obtain a neural network-based model of a real autonomous vehicle
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In this paper, a set of Radial Basis Function (RBF)neural networks, capable to learn the kinematic and dynamic behavior of the Romeo 4R autonomous vehicle, is presented. In order to obtain a set of good RBF nets in terms of the number of neurons and the number of lagged inputs, a Multi-Objective Genetic Algorithm (MOGA) has been used. The kinematic and dynamic systems of the mobile robot have been split into three
subsystems: the steering module, the drive module and the heading module. Each subsystem is modeled with a neural network that learns its behaviour using, among others, a set of lagged outputs as inputs. The outputs from the steering and drive
modules are also used as inputs in the heading module. Neural networks - based models are compared to classical approaches.
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Pulido, Nieves Pavon; Melero, Joaquin Ferruz; Ruano, A. E. Using a genetic algorithm to obtain a neural network-based model of a real autonomous vehicle, Trabalho apresentado em 2008 IEEE International Symposium on Industrial Electronics (ISIE 2008), In Proceedings of the 2008 IEEE International Symposium on Industrial Electronics, Cambridge, UK, 2008.