A framework for simulation-based optimization demonstrated on reconfigurable robot workcells

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 2017-10-01 DOI:10.1109/SYSENG.2017.8088278

L. Atorf, C. Schorn, J. Rossmann, Christian Schlette

{"title":"A framework for simulation-based optimization demonstrated on reconfigurable robot workcells","authors":"L. Atorf, C. Schorn, J. Rossmann, Christian Schlette","doi":"10.1109/SYSENG.2017.8088278","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":354846,"journal":{"name":"2017 IEEE International Systems Engineering Symposium (ISSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Systems Engineering Symposium (ISSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYSENG.2017.8088278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

Abstract

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.

查看原文本刊更多论文

基于可重构机器人工作单元的仿真优化框架

在新兴的工业4.0范式转变的推动下，当今的自动化和机器人趋势带来了许多新的控制和优化问题。与此同时，三维仿真技术的进步导致各个领域的仿真模型和算法不断改进，如多体动力学、运动学或传感器仿真。这种发展可以用于基于仿真的优化(SBO)，其中优化结果可以直接从仿真模型转移到现实世界。本文介绍了模型配置和SBO的形式化和模块化框架。我们通过优化H2020项目ReconCell中可重构机器人工作单元中的传感器布局来展示我们框架的功能，允许工程师使用不同的优化器和参数进行实验。对结果的评估证明了该方法的有效性，并表明该框架可以在不限制仿真环境选择的情况下应用于广泛的优化问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2017 IEEE International Systems Engineering Symposium (ISSE)

自引率

0.00%

发文量