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Wyszukujesz frazę "Blumberg, Julian" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Estimation of external force-torque vector based on double encoders of industrial robots using a hybrid Gaussian process regression and joint stiffness model
Autorzy:
Uhlmann, Eckart
Polte, Mitchel
Blumberg, Julian
Powiązania:
https://bibliotekanauki.pl/articles/24084712.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
industrial robot
double encoders
force-torque estimation
Opis:
Industrial robots are increasingly used in industry for contact-based manufacturing processes such as milling and forming. In order to meet part tolerances, it is mandatory to compensate tool deflections caused by the external force-torque vector. However, using a third-party measuring device for sensing the external force-torque vector lowers the cost efficiency. Novel industrial robots are increasingly equipped with double encoders, in order to compensate deviations caused by the gearboxes. This paper proposes a method for the usage of such double encoders to estimate the external force-torque vector acting at the tool centre point of an industrial robot. Therefore, the joint elasticities of a six revolute joint industrial robot are identified in terms of piecewise linear functions based on the angular deviations at the double encoders when an external force-torque vector is applied. Further, initial deviations between the encoder values caused by gravitational forces and friction are modelled with a Gaussian process regression. Combining both methods to a hybrid model enables the estimation of external force-torque vectors solely based on measurements of the joint angles of secondary encoders. Based on the proposed method, additional measurement equipment can be saved, which reduces investment costs and improves robot dynamics.
Źródło:
Journal of Machine Engineering; 2023, 23, 3; 56--68
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hyperparameter optimization of artificial neural networks to improve the positional accuracy of industrial robots
Autorzy:
Uhlmann, Eckart
Polte, Mitchel
Blumberg, Julian
Li, Zhoulong
Kraft, Adrian
Powiązania:
https://bibliotekanauki.pl/articles/1429023.pdf
Data publikacji:
2021
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
artificial neural network
robot calibration
hyperparameter optimization
Opis:
Due to the rising demand for individualized product specifications and short product innovation cycles, industrial robots gain increasing attention for machining operations as milling and forming. Limitations in their absolute positional accuracy are addressed by enhanced modelling and calibration techniques. However, the resulting absolute positional accuracy stays in a range still not feasible for general purpose milling and forming tolerances. Improvements of the model accuracy demand complex, often not accessible system knowledge on the expense of realtime capability. This article presents a new approach using artificial neural networks to enhance positional accuracy of industrial robots. A hyperparameter optimization is applied, to overcome the downside of choosing an appropriate artificial neural network structure and training strategy in a trial and error procedure. The effectiveness of the method is validated with a heavy-duty industrial robot. It is demonstrated that artificial neural networks with suitable hyperparameters outperform a kinematic model with calibrated geometric parameters.
Źródło:
Journal of Machine Engineering; 2021, 21, 2; 47-59
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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