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Tracking Trajectory of the Mobile Robot Khepera II Using Approaches of Artificial Intelligence

DOI: 10.2478/v10198-011-0006-y

Keywords: mobile robot, MLP neural network, forward neural model, inverse neural model, genetic algorithm, tracking trajectory

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This paper introduces a solution to the reference trajectory tracking problem done by a differential wheeled mobile robot Khepera II. The paper includes a kinematic part and a dynamic part of the mathematical model of mobile robot. In this paper two approaches of the artificial intelligence are used i.e. genetic algorithm approach from evolutionary computing techniques and theory of neural networks. Genetic algorithm is used for parameters optimizing PID controller and K parameter so-called parameter speed of rotation at the tracking reference trajectory into defined control structure. For the creation forward and inverse neural model by the approach of neural networks are used forward neural networks of MLP type. The neural models are verified using Neural Network Toolbox. The forward neural model of the mobile robot is implemented into the IMC control structure together with the inverse neural model, which is used as a nonparametric neural controller. The purpose of the designed control structure is tracking the defined trajectory of the mobile robot using approaches of the artificial intelligence, which are verified by the simulations in the language Matlab/Simulink.


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