Heavy Changes in the Input Flow for Learning Geography of a Robot Environment

摘要

A novel approach to generation of geographic knowledge from robot views is presented. It is implemented in a pilot software where a virtual robot operates in a static 2D-environment. The robot sensor scans with rays an angular field of view and produces a ID view of distances to the closest obstacle. By processing such views, 'heavy changes' are detected to trigger switching local maps in an atlas that represents geography of the robot environment. To detect heavy changes, firstly, each plot is transformed to a string of singular points; then, in time-scale, a pair of such strings is subjected to a treatment based on application of the distance of Levenshtein, which leads to so-called Editorial Prescription (EP); a heavy change is detected if EP shows a considerable distinction between strings. This approach is applied in automatic construction of an atlas for non-Cartesian navigation, while robot explores the scene.