De Nardi, R;
Nonlinear Dynamics Modelling for Controller Evolution.
GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2.
(pp. 324 - 331).
ASSOC COMPUTING MACHINERY
The problem of how to acquire a model of a physical robot, which is fit for evolution of controllers that can subsequently be used to control that robot, is considered in the context, of racing a radio-controlled toy car around a randomised track. Several modelling techniques are compared, and the specific properties of the acquired models that; influence the quality of the evolved controller are discussed. As we ann to minimise the amount; of domain knowledge used., we further investigate the relation between the assumptions about the modelled system made by particular modelling techniques and the suitability of the acquired models as bases for controller evolution. We find that none of the models acquired is good enough on its own, and that a key, to evolving robust behaviour is to evaluate controllers simultaneously on multiple models during evolution. Examples of successfully evolved racing control for the physical car are analysed.
|Title:||Nonlinear Dynamics Modelling for Controller Evolution|
|Event:||Annual Conference of Genetic and Evolutionary Computation Conference|
|Dates:||2007-07-07 - 2007-07-11|
|Keywords:||Evolutionary robotics, neural networks, forward models, system identification, games|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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