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Neural network learning of variable grid-based maps for the autonomous navigation of robots

This paper presents a map learning method that integrates the geometrical and topological paradigms. The geometrical component consists of a feed-forward neural network that interprets the robot's sensor readings efficiently. The topological map is created by learning a variable resolution partitioning of the world. Every partition corresponds to a perceptually homogeneous region. The efficiency of the learning process is based on the use of local memory-based techniques for partitioning and of active learning techniques for selecting the most appropriate region to be explored next. Finally, the paper reports experimental results obtained with the autonomous mobile robot TESEO.