This paper presents an approach to estimating the contact state between a robot and its environment during task execution. Contact states are modeled by constraint equations parameterized by time-dependent sensor data and time-independent object properties. At each sampling time, multiple model estimation is used to assess the most likely contact state. The assessment is performed by a Hidden Markov Model, which combines a measure of how well each set of constraint equations fit the sensor data with the probability of specific contact state transitions. The latter is embodied in a task-based contact state network. The approach is illustrated for a three dimensional peg-in-hole insertion using a tabletop manipulator robot. Using only position sensing, the contact state sequence is successfully estimated. Property estimates are obtained for the peg dimensions as well as the hole position and orientation.


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    Titel :

    Contact State Estimation using Multiple Model Estimation and Hidden Markov Models


    Beteiligte:
    Siciliano, Bruno (Herausgeber:in) / Dario, Paolo (Herausgeber:in) / Debus, Thomas (Autor:in) / Dupont, Pierre (Autor:in) / Howe, Robert (Autor:in)


    Erscheinungsdatum :

    30.06.2003


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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