A framework for simulation-based optimization demonstrated on reconfigurable robot workcells
| Autor: | Christian Schlette, Linus Atorf, Juergen Rossmann, Christoph Schorn |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Optimization 0209 industrial biotechnology Engineering Optimization problem Virtual Testbeds 030106 microbiology 02 engineering and technology Solid modeling 03 medical and health sciences 020901 industrial engineering & automation Simulation-based optimization Robot Workcell Workcell Simulation-based Optimization Simulation Models business.industry Control engineering Robotics Modular design Automation Virtual Commissioning Robot Artificial intelligence business Simulation eRobotics |
| Zdroj: | Atorf, L, Schorn, C, Roßmann, J & Schlette, C 2017, A framework for simulation-based optimization demonstrated on reconfigurable robot workcells . in Proceedings of the 2017 IEEE International Systems Engineering Symposium . IEEE, 2017 IEEE International Systems Engineering Symposium, Vienna, Austria, 11/10/2017 . https://doi.org/10.1109/SysEng.2017.8088278 |
| DOI: | 10.1109/SysEng.2017.8088278 |
| Popis: | Today's trends towards automation and robotics, fueled by the emerging Industry 4.0 paradigm shift, open up many new kinds of control and optimization problems. At the same time, advances in 3D simulation technology lead to ever-improving simulation models and algorithms in various domains, such as multi-body dynamics, kinematics, or sensor simulation. This development can be harnessed for Simulation- based Optimization (SBO), where optimization results can be directly transferred from simulation models to the real world. In this paper, we introduce a formalism and modular framework for model configuration and SBO. We demonstrate the capabilities of our framework by optimizing the sensor layout within a reconfigurable robot workcell from the H2020 project ReconCell, allowing engineers to experiment with different optimizers and parameters. Evaluation of the results proves the usefulness of our approach and shows that the framework can be applied to a wide range of optimization problems without constraining the choice of simulation environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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