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.

George Palamas and J. Andrew Ware, “Sub-goal based Robot Visual Navigation through Sensorial Space Tesselation” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(11), 2013. http://dx.doi.org/10.14569/IJARAI.2013.021106

@article{Palamas2013,
title = {Sub-goal based Robot Visual Navigation through Sensorial Space Tesselation},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2013.021106},
url = {http://dx.doi.org/10.14569/IJARAI.2013.021106},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {11},
author = {George Palamas and J. Andrew Ware}
}