In the robotics community, localization is considered a solved problem, however, the topic is still open to investigation. Mobile robot localization has been focused on developing low-cost approaches and there has been great success using probabilistic methods. Parallel to this, and to a much lesser extent, artificial neural networks (ANNs) have been applied to the problem area with varying success.A system is proposed in this thesis where the typical probabilistic approach is replaced with one based purely on ANNs. This type of localization attempts to harness the simplicity, scalability and adaptability that ANNs are known for. The ANN approach allows for the encapsulation of a number of steps and elements well known in a prob- abilistic approach, resulting in the elimination of an internal explicit map, providing pose estimate on network output and network update at runtime. First, a coordinate-based approach to localization is explored: 1D and 2D trained maps with pose estimates. Second, the coordinate-based approach is eliminated in an effort to replicate a more biologically inspired localization. Finally, a path-finding algorithm applying the new localizaiton approach is presented. ; Validerat; 20121122 (global_studentproject_submitter)


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    Title :

    Lokalisering av mobila robotar medelst artificiella neurala nätverk


    Contributors:

    Publication date :

    2012-01-01


    Remarks:

    Local 98ef20bc-a72b-4faa-9074-278acfd2fc68


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629