A Machine Learning approach for error recovery in an industrial robot for the plastic mold industry isproposed in this master thesis project. The goal was to improve the present error recovery method byproviding a learning algorithm to the system instead of using the traditional algorithm-based control.The chosen method was the Support Vector Machine (SVM) due to the robustness and the goodgeneralization performance in real-world applications. Furthermore, SVM generates good classifierseven with a minimal number of training examples. In production, there will be no need for a humanoperator to train the SVM with hundreds or thousands of training examples to achieve goodgeneralization. The advantage with SVM is that good accuracy can be achieved with only a couple oftraining examples if the training examples are well designed.Firstly, the algorithm proposed was evaluated experimentally. The experiments consisted of correcthandling of classification performance on training examples, which was a hand-coded data set createdwith defined in- and output signals. Secondly, the results from the experiments were tested in asimulated environment. By using only a few training examples the SVM reached perfect performance.In conclusion, SVM is a good tool for classification and a suitable method for error recovery on theindustrial robot for the plastic mold industry. ; En maskininlärningsstrategi för felhantering på industrirobotar inom plastformindustrin presenteras idetta examensarbete. Målet var att förbättra den nuvarande felhanteringen genom att applicera eninlärningsalgoritm istället för det traditionella förprogrammerade systemet till roboten. Den valdametoden är Support Vector Machine (SVM), då SVM är en robust metod som ger bra prestanda iverkliga tillämpningar. SVM genererar bra klassificerare även med ett minimalt antal träningsexempel.Fördelen med SVM är att god precision kan uppnås med bara ett par träningsexempel förutsatt attträningsexemplen är väldesignade. Detta betyder att operatörerna i produktionen inte ...


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    Titel :

    Support Vector Machine Algorithm applied to Industrial Robot Error Recovery ; Support Vector Machine algoritm tillämpad inom felhantering på industrirobotar


    Beteiligte:
    Lau, Cidney (Autor:in)

    Erscheinungsdatum :

    2015-01-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629





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