In modern collaborative production environments where industrial robots and humans are supposed to work hand in hand, it is mandatory to observe the robot’s workspace at all times. Such observation is even more crucial when the robot’s main position is also dynamic e.g. because the system is mounted on a movable platform. As current solutions like physically secured areas in which a robot can perform actions potentially dangerous for humans, become unfeasible in such scenarios, novel, more dynamic, and situation aware safety solutions need to be developed and deployed. This thesis mainly contributes to the bigger picture of such a collaborative scenario by presenting a data-driven convolutional neural network-based approach to estimate the two-dimensional kinematic-chain configuration of industrial robot-arms within raw camera images. This thesis also provides the information needed to generate and organize the mandatory data basis and presents frameworks that were used to realize all involved subsystems. The robot-arm’s extracted kinematic-chain can also be used to estimate the extrinsic camera parameters relative to the robot’s three-dimensional origin. Further a tracking system, based on a two-dimensional kinematic chain descriptor is presented to allow for an accumulation of a proper movement history which enables the prediction of future target positions within the given image plane. The combination of the extracted robot’s pose with a simultaneous human pose estimation system delivers a consistent data flow that can be used in higher-level applications. This thesis also provides a detailed evaluation of all involved subsystems and provides a broad overview of their particular performance, based on novel generated, semi automatically annotated, real datasets.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Two-dimensional pose estimation of industrial robotic arms in highly dynamic collaborative environments


    Beteiligte:
    Gulde, Thomas (Autor:in)

    Erscheinungsdatum :

    2022


    Format / Umfang :

    1 Online-Ressource (124, circa 30 Seiten)


    Anmerkungen:

    Illustrationen



    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Object Detection and 6D Pose Estimation for Precise Robotic Manipulation in Unstructured Environments

    di Castro, Mario / Camarero Vera, Jorge / Ferre, Manuel et al. | Springer Verlag | 2019


    Dynamic ego-pose estimation for driver assistance in urban environments

    Cech, M. / Niem, W. / Abraham, S. et al. | IEEE | 2004


    Nonlinear Dynamic Analysis of Elastic Robotic Arms

    Wen, Hongbing / Jiang, Shanying | Springer Verlag | 2023


    MAP1.04 Dynamic Ego-Pose Estimation for Driver Assistance in Urban Environments

    Cech, M. / Niem, W. / Abraham, S. et al. | British Library Conference Proceedings | 2004