With the development of science and technology, the research for mobile robot has reached a new stage, and the intelligent level of the robot has been improved greatly. Neural Networks have developed greatly, and spiking neural networks (SNNs) - the third generation of artificial neural networks (ANNs) appeared. SNNs have advantages over the previous two generations of ANNs in many aspects, because they have more plausible neural models than the previous ones. SNNs have attracted great attentions in neural networks research area. This dissertation focuses on mobile robot's behavior control and environmental perception, which are based on SNNs. The main contributions of this dissertation are as follows: Using multi-sonar-sensor-information, the design of the behavior controller based on SNNs for mobile robot to avoid obstacles is proposed. The structure of the controller is easy to implement and the SNN in the controller can be tuned on line by spike-based Hebbian learning ruler. The simulation results are analyzed and discussed, which show the controller is effective. The wall-following controller based on SNNs is also proposed, and the simulation results show that the robot can follow walls very well by the controller. Combined with the obstacle-avoidance controller and the wall-following controller, which are based on SNNs, the navigation controller is designed. The navigation simulations involving composite behaviors such as obstacles avoidance, wall trace and the objective point approximation, verified the effectiveness of the sensor-based autonomous navigation controller of mobile robot in unknown and unstructured environments. With multi-sensor information fusion, such intelligent computing methods as: SNNs, PCA, Kernel-PCA, and BP NNs, are applied in mobile robot's environmental perception. Using the above methods, classifiers for the seven commonly corridor scenes are designed. The classifying results are then analyzed and compared. The classifiers based on Kernel-PCA and SNNs all have higher corridor scenes recognization rate and better robustness.
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