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Abstract-In this paper, we propose an evolutionary cognitive architecture to enable a mobile robot to cope with the task of visual navigation. Initially a graph based world representation is used to build a map, prior to navigation, through an appearance based scheme using only features associated with color information. During the next step, a genetic algorithm evolves a navigation controller that the robot uses for visual servoing, driving through a set of nodes on the topological map. Experiments in simulation show that an evolved robot, adapted to both exteroceptive and proprioceptive data, is able to successfully drive through a list of sub-goals minimizing the problem of local minima in which evolutionary process can sometimes get trapped. We also show that this approach is more expressive for defining a simplistic fitness formula yet descriptive enough for targeting specific goals.

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The structure of this thesis is shown in the table of contents below along with a complete list of figures and tables. In addition, abbreviations are used throughout the body of text. These are all explained in the table below. Important appendices will be included in the end of the thesis, and additional ones can be found online. These are referred to as "Electronic Appendices".