Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560546
Kilian Telschig, Andreas Schonberger, Alexander Knapp
Container technologies such as Docker and Linux Containers (lxc) have become common tools in modern software engineering practice. They provide a dynamic and lightweight mechanism for software isolation and resource control, e.g. for continuous integration jobs or as app execution context. We adapt containers to industrial domains to offer enhanced reliability and legacy compatibility for distributed embedded applications. We describe a cross-domain real-time container architecture for dependable distributed embedded applications with criticality of timing requirements ranging from hard to non real-time. Through containers the proposed architecture isolates the software components from the system and from each other and only provides resources and inter-component communication explicitly demanded in each component's description. This enforces the interfaces and enables quality assurance and legacy compatibility. We provide a platform-independent model of the real-time container architecture but also describe a concrete lxc-based realization which conforms to this model.
{"title":"A Real-Time Container Architecture for Dependable Distributed Embedded Applications","authors":"Kilian Telschig, Andreas Schonberger, Alexander Knapp","doi":"10.1109/COASE.2018.8560546","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560546","url":null,"abstract":"Container technologies such as Docker and Linux Containers (lxc) have become common tools in modern software engineering practice. They provide a dynamic and lightweight mechanism for software isolation and resource control, e.g. for continuous integration jobs or as app execution context. We adapt containers to industrial domains to offer enhanced reliability and legacy compatibility for distributed embedded applications. We describe a cross-domain real-time container architecture for dependable distributed embedded applications with criticality of timing requirements ranging from hard to non real-time. Through containers the proposed architecture isolates the software components from the system and from each other and only provides resources and inter-component communication explicitly demanded in each component's description. This enforces the interfaces and enables quality assurance and legacy compatibility. We provide a platform-independent model of the real-time container architecture but also describe a concrete lxc-based realization which conforms to this model.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"100 1","pages":"1367-1374"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87005260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560458
Felix Spenrath, A. Pott
The fast determination of collision-free grasps is a key aspect in random bin picking. Heuristic search algorithms provide a feasible solution to this problem, using statistical data on the likelihood of finding a valid solution on elements with certain parameters. In this paper, we propose the use of several neural networks in such algorithms to accelerate the search while preserving the reliability. This is done by training the neural networks on the heuristic search trees of previous situations and using the output of these neural networks as part of the heuristic function. Finally, the effect of these neural networks is experimentally analyzed with sensor data from a working bin picking system with an industrial dual arm robot and it is shown that the calculation time in this setup is reduced by up to 45%.
{"title":"Using Neural Networks for Heuristic Grasp Planning in Random Bin Picking","authors":"Felix Spenrath, A. Pott","doi":"10.1109/COASE.2018.8560458","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560458","url":null,"abstract":"The fast determination of collision-free grasps is a key aspect in random bin picking. Heuristic search algorithms provide a feasible solution to this problem, using statistical data on the likelihood of finding a valid solution on elements with certain parameters. In this paper, we propose the use of several neural networks in such algorithms to accelerate the search while preserving the reliability. This is done by training the neural networks on the heuristic search trees of previous situations and using the output of these neural networks as part of the heuristic function. Finally, the effect of these neural networks is experimentally analyzed with sensor data from a working bin picking system with an industrial dual arm robot and it is shown that the calculation time in this setup is reduced by up to 45%.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"258-263"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85912116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560588
Suhyun Cha, A. Weigl, Mattias Ulbrich, Bernhard Beckert, B. Vogel‐Heuser
Automated production systems (aPS) operate for a long time with continuous and incremental changes. However, the models for aPS have not been maintained along with these system changes or, even, have not been properly generated. Even though the regression verification technique reduces the effort of applying formal verification on the automation system evolution, there still remains what should be provided in a formal form for the verification: delta, which is the difference of the two versions of the software. In this paper, we propose a method for generating a formal model from preexisting software in IEC 61131–3 Sequential Function Chart language. Based on this, the developer is able to achieve delta description by revising it to reflect the change request and this formal description of delta could facilitate verifying delta formally.
{"title":"Achieving delta description of the control software for an automated production system evolution","authors":"Suhyun Cha, A. Weigl, Mattias Ulbrich, Bernhard Beckert, B. Vogel‐Heuser","doi":"10.1109/COASE.2018.8560588","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560588","url":null,"abstract":"Automated production systems (aPS) operate for a long time with continuous and incremental changes. However, the models for aPS have not been maintained along with these system changes or, even, have not been properly generated. Even though the regression verification technique reduces the effort of applying formal verification on the automation system evolution, there still remains what should be provided in a formal form for the verification: delta, which is the difference of the two versions of the software. In this paper, we propose a method for generating a formal model from preexisting software in IEC 61131–3 Sequential Function Chart language. Based on this, the developer is able to achieve delta description by revising it to reflect the change request and this formal description of delta could facilitate verifying delta formally.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"47 1","pages":"1170-1176"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82859060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560477
Hongyue Sun, Giulia Pedrielli, Guanglei Zhao, Andrea Bragagnolo, Chi Zhou, R. Pan, Wenyao Xu
This paper extends the conventional single-stage additive manufacturing (AM) processes to multi-STage distRibutEd AM systems (STREAMs). In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the centralized post-processing. Such systems are widely encountered in AM processes such as energy-AM, metal-AM and bio-AM. Modeling and managing such complex systems have been challenging. We propose a novel framework for “cyber-coordinated simulation” to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where single stage, multi-stage and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from freeze nano printing AM processes.
{"title":"Cyber-coordinated Simulation Models for Multi-stage Additive Manufacturing of Energy Products","authors":"Hongyue Sun, Giulia Pedrielli, Guanglei Zhao, Andrea Bragagnolo, Chi Zhou, R. Pan, Wenyao Xu","doi":"10.1109/COASE.2018.8560477","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560477","url":null,"abstract":"This paper extends the conventional single-stage additive manufacturing (AM) processes to multi-STage distRibutEd AM systems (STREAMs). In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the centralized post-processing. Such systems are widely encountered in AM processes such as energy-AM, metal-AM and bio-AM. Modeling and managing such complex systems have been challenging. We propose a novel framework for “cyber-coordinated simulation” to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where single stage, multi-stage and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from freeze nano printing AM processes.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"73 1","pages":"893-898"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89897363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560366
Z. Fei, Shiqi Li, Q. Chang, Junfeng Wang, Yaqin Huang
In a manufacturing system, the idle status of machine consuming huge amounts of energy cannot bring any added value. How to reduce the energy waste of idle period through the real time control of machine status has become a challenging goal in an energy-efficient manufacturing environment. To address this problem, we propose a fuzzy Petri net based fuzzy reasoning approach to reduce the idle period by switching the on/off status of machines. The approach uses the real time data collected from the system, which include the level of upstream and downstream buffers, as well as the working status of the machine. The fuzzy rules are described by analyzing the decision intention according to the human knowledge. Simulation experiments show that this approach can effectively reduce the energy consumption with accepted throughput loss for a serial manufacturing system.
{"title":"Fuzzy Petri Net Based Intelligent Machine Operation of Energy Efficient Manufacturing System","authors":"Z. Fei, Shiqi Li, Q. Chang, Junfeng Wang, Yaqin Huang","doi":"10.1109/COASE.2018.8560366","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560366","url":null,"abstract":"In a manufacturing system, the idle status of machine consuming huge amounts of energy cannot bring any added value. How to reduce the energy waste of idle period through the real time control of machine status has become a challenging goal in an energy-efficient manufacturing environment. To address this problem, we propose a fuzzy Petri net based fuzzy reasoning approach to reduce the idle period by switching the on/off status of machines. The approach uses the real time data collected from the system, which include the level of upstream and downstream buffers, as well as the working status of the machine. The fuzzy rules are described by analyzing the decision intention according to the human knowledge. Simulation experiments show that this approach can effectively reduce the energy consumption with accepted throughput loss for a serial manufacturing system.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"116 6 Vyp 2. Neurology and psychiatry of elderly 1","pages":"1593-1598"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90242732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560464
Lianlian Zhang, F. Qiao, Junkai Wang
With the rapid development of Internet-of-Things and big data, health assessment of equipment has become a hot spot in recent years. It is critical to bridge the gap between real-time factory data and health status evaluation, which helps decide appropriate maintenance time by quantitative fault-early warning. For this purpose, this paper proposes a framework to realize real-time equipment health management. The framework begins with principal component analysis (PCA) for feature reduction and support vector data description (SVDD) method for identifying abnormal observations. To promote the computational efficiency of the static health assessment model, an improved incremental learning SVDD method based on KKT (Karush-Kuhn-Tucker) condition (KISVDD) is proposed. Then health degree (HD) is defined derived from deviation degree (DD) based on Euclidean distance. Subsequently, a fault-early warning threshold setting method based on sliding window is established to realize quantitative maintenance time prediction. Thereafter, the proposed scheme is compared with different types of algorithms in a case study to demonstrate the effectiveness of the proposed model using actual production data. The results show that the proposed model outperforms traditional ones in accuracy and computational efficiency.
{"title":"Equipment health assessment and fault-early warning algorithm based on improved SVDD","authors":"Lianlian Zhang, F. Qiao, Junkai Wang","doi":"10.1109/COASE.2018.8560464","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560464","url":null,"abstract":"With the rapid development of Internet-of-Things and big data, health assessment of equipment has become a hot spot in recent years. It is critical to bridge the gap between real-time factory data and health status evaluation, which helps decide appropriate maintenance time by quantitative fault-early warning. For this purpose, this paper proposes a framework to realize real-time equipment health management. The framework begins with principal component analysis (PCA) for feature reduction and support vector data description (SVDD) method for identifying abnormal observations. To promote the computational efficiency of the static health assessment model, an improved incremental learning SVDD method based on KKT (Karush-Kuhn-Tucker) condition (KISVDD) is proposed. Then health degree (HD) is defined derived from deviation degree (DD) based on Euclidean distance. Subsequently, a fault-early warning threshold setting method based on sliding window is established to realize quantitative maintenance time prediction. Thereafter, the proposed scheme is compared with different types of algorithms in a case study to demonstrate the effectiveness of the proposed model using actual production data. The results show that the proposed model outperforms traditional ones in accuracy and computational efficiency.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"56 12","pages":"716-721"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91501659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560521
Dong-Hyun Roh, Tae-Eog Lee
Cluster tools are widely used manufacturing equipment in semiconductor manufacturing systems and consist of several process chambers, loadlock modules, and a wafer transport robot. The operation of the cluster tool relies on decision making about the robot operations. Generally, a robot iteratively determines its next task according to a given task sequence. This tool schedule is called a cyclic schedule. If the same timing pattern repeats every $K$ work cycles in a cyclic schedule, the schedule is called a $K$ -cyclic schedule. In a cluster tool with a $K$ -cyclic schedule, wafer delay, which is the time that a processed wafer is stored in the process chamber, becomes an important issue. In this study, we identify the worst-case wafer delay, which is the maximum value of wafer delay among all the $K$ -cyclic schedules a cluster tool can have. To do this, we present timed event graph models for dual-armed and single-armed cluster tools and briefly explain the previous research on closed-form formulae of token delays in timed event graphs with K-cyclic schedules suggested by Lee et al. [1]. Finally, we propose a method for deriving a closed-form formula for the worst-case wafer delay in a cluster tool, which can be applied to arbitrary wafer flow patterns and time parameters.
{"title":"Characterizing the Worst-Case Wafer Delay in a Cluster Tool Operated in a $K$-Cyclic Schedule","authors":"Dong-Hyun Roh, Tae-Eog Lee","doi":"10.1109/COASE.2018.8560521","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560521","url":null,"abstract":"Cluster tools are widely used manufacturing equipment in semiconductor manufacturing systems and consist of several process chambers, loadlock modules, and a wafer transport robot. The operation of the cluster tool relies on decision making about the robot operations. Generally, a robot iteratively determines its next task according to a given task sequence. This tool schedule is called a cyclic schedule. If the same timing pattern repeats every $K$ work cycles in a cyclic schedule, the schedule is called a $K$ -cyclic schedule. In a cluster tool with a $K$ -cyclic schedule, wafer delay, which is the time that a processed wafer is stored in the process chamber, becomes an important issue. In this study, we identify the worst-case wafer delay, which is the maximum value of wafer delay among all the $K$ -cyclic schedules a cluster tool can have. To do this, we present timed event graph models for dual-armed and single-armed cluster tools and briefly explain the previous research on closed-form formulae of token delays in timed event graphs with K-cyclic schedules suggested by Lee et al. [1]. Finally, we propose a method for deriving a closed-form formula for the worst-case wafer delay in a cluster tool, which can be applied to arbitrary wafer flow patterns and time parameters.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"25 1","pages":"1562-1567"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81074389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560359
S. Löw, D. Obradovic
Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.
{"title":"Real-time Implementation of Nonlinear Model Predictive Control for Mechatronic Systems Using a Hybrid Model","authors":"S. Löw, D. Obradovic","doi":"10.1109/COASE.2018.8560359","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560359","url":null,"abstract":"Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"18 1","pages":"164-167"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81968891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560471
Christian Lieberoth-Leden, J. Fischer, J. Fottner, B. Vogel‐Heuser
The modularization of hard- and software is one approach to handle the demand for increasing flexibility and changeability of automated material flow systems that are, for example, utilized in flexible production systems. In such automated material flow systems, autonomous modules communicate with each other to coordinate and execute transport tasks. The modules are able to detect neighbouring modules and configure interfaces. A control architecture with a central coordination instance is proposed to efficiently communicate topology, state and planning information in a multi-agent material flow system. Furthermore, a planning and scheduling concept for the material flow control is introduced which optimizes traffic and fulfils material flow requirements such as sequencing.
{"title":"Control Architecture and Transport Coordination for Autonomous Logistics Modules in Flexible Automated Material Flow Systems","authors":"Christian Lieberoth-Leden, J. Fischer, J. Fottner, B. Vogel‐Heuser","doi":"10.1109/COASE.2018.8560471","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560471","url":null,"abstract":"The modularization of hard- and software is one approach to handle the demand for increasing flexibility and changeability of automated material flow systems that are, for example, utilized in flexible production systems. In such automated material flow systems, autonomous modules communicate with each other to coordinate and execute transport tasks. The modules are able to detect neighbouring modules and configure interfaces. A control architecture with a central coordination instance is proposed to efficiently communicate topology, state and planning information in a multi-agent material flow system. Furthermore, a planning and scheduling concept for the material flow control is introduced which optimizes traffic and fulfils material flow requirements such as sequencing.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"93 1","pages":"736-743"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79437119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560496
Jason Li, Jonatan Berglund, Felix Auris, Atieh Hanna, J. Vallhagen, K. Åkesson
A digital twin of a production system consists of geometric, kinematic and logical models of the physical system. One of the key challenges is to keep the digital twin up-to-date with changes of the real one. Today, laser scanning is the de-facto standard used to keep the geometry of the digital model synchronized. In recent years, advancements in the performance of Graphic Processing Units (GPUs) and the availability of cheap high-resolution digital cameras have made photogrammetry a viable alternative to laser scanning for building digital 3D-models. In this study, we investigate how photogrammetry competes against laser-scanning by comparing their results in form of point-clouds.
{"title":"Evaluation of Photogrammetry for Use in Industrial Production Systems","authors":"Jason Li, Jonatan Berglund, Felix Auris, Atieh Hanna, J. Vallhagen, K. Åkesson","doi":"10.1109/COASE.2018.8560496","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560496","url":null,"abstract":"A digital twin of a production system consists of geometric, kinematic and logical models of the physical system. One of the key challenges is to keep the digital twin up-to-date with changes of the real one. Today, laser scanning is the de-facto standard used to keep the geometry of the digital model synchronized. In recent years, advancements in the performance of Graphic Processing Units (GPUs) and the availability of cheap high-resolution digital cameras have made photogrammetry a viable alternative to laser scanning for building digital 3D-models. In this study, we investigate how photogrammetry competes against laser-scanning by comparing their results in form of point-clouds.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"21 1","pages":"414-420"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78394665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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