Abstract
This paper presents a way of implementing a Model Based Predictive Controller (MBPC) for mobile robot navigation when unexpected static obstacles are present in the robot environment. The method uses a non-linear model of mobile robot kinematics and thus allows an accurate prediction of the future trajectories. An ultrasonic ranging system has been used for obstacle detection. A Multilayer Perceptron is used to implement the MBPC, allowing real-time and also eliminating the need for data sensor high level processing. The perceptron has been trained to reproduce the MBPC behaviour in a supervised manner. Experimented results obtained when applying the neural network controller to a mobile robot are given in the paper.
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