Dynamic movement primitives (DMP) are an efficient way for learning and reproducing complex robot behaviors. A singularity free DMP formulation for orientation in the Cartesian space is proposed by Ude et al. in 2014 and has been largely adopted by the research community. In this work, we demonstrate the undesired oscillatory behavior that may arise when controlling the robot’s orien- tation with this formulation, producing a motion pattern highly deviant from the desired and highlight its source. A correct formulation is then proposed that alle- viates such problems while guaranteeing generation of orientation parameters that lie in SO(3). We further show that all aspects and advantages of DMP including ease of learning, temporal and spatial scaling and the ability to include coupling terms are maintained in the proposed formulation. Simulations and experiments with robot control in SO(3) are performed to demonstrate the performance of the proposed formulation and compare it with the previously adopted one.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A correct formulation for the Orientation Dynamic Movement Primitives for robot control in the Cartesian space


    Beteiligte:
    Koutras, Leonidas (Autor:in) / Doulgeri, Zoe (Autor:in)

    Erscheinungsdatum :

    2019-11-01



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Gesture learning and execution in a humanoid robot via dynamic movement primitives

    Pfeiffer, Sammy / Angulo Bahón, Cecilio | BASE | 2015

    Freier Zugriff

    Overcoming some drawbacks of Dynamic Movement Primitives

    Michele Ginesi / Nicola Sansonetto / Paolo Fiorini | BASE | 2021

    Freier Zugriff

    Conditional Neural Movement Primitives

    M. Yunus Seker / Mert Imre / Justus Piater et al. | BASE | 2019

    Freier Zugriff

    Lunar Excavator Mission Operations using Dynamic Movement Primitives

    Joseph M. Cloud / Minh Q. Tram / William J. Beksi et al. | NTRS


    Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

    Michele Ginesi / Daniele Meli / Andrea Roberti et al. | BASE | 2021

    Freier Zugriff