Pub Date : 2018-07-01DOI: 10.1109/INDIN.2018.8471974
Rogério Casagrande, R. Moraes, C. Montez, A. S. Morales, Luciana Rech
Wireless Body Area Networks (WBANs) must comply with stringent communication requirements. One of these requirements is reliable data delivery. However, it is well-know that wireless networks operating in the same frequency range and using overlapping channels, tend to experience interferences that may compromise the network Quality of Service. This paper presents an experimental assessment of the interference of IEEE 802.11n networks upon IEEE 802.15.4-based WBANs, operating in the 2.4 GHz frequency band. A set of experimental tests were performed, in compliance with the IEEE 802.15.2 standard to assess the coexistence of IEEE 802.15.4 and IEEE 802.11n nodes, operating in overlapping and non-overlapping communication channels. The performed experimental tests considered different distances between the IEEE 802.11n access point and the IEEE 802.15.4 coordinator, as well as different communication channels, and different IEEE 802.11n network data rates. The experimental results highlight a strong interference between both networks, with a clear impact upon the communication reliability.
{"title":"Interference of IEEE 802.11n Networks upon IEEE 802.15.4-Based WBANs: An Experimental Study","authors":"Rogério Casagrande, R. Moraes, C. Montez, A. S. Morales, Luciana Rech","doi":"10.1109/INDIN.2018.8471974","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8471974","url":null,"abstract":"Wireless Body Area Networks (WBANs) must comply with stringent communication requirements. One of these requirements is reliable data delivery. However, it is well-know that wireless networks operating in the same frequency range and using overlapping channels, tend to experience interferences that may compromise the network Quality of Service. This paper presents an experimental assessment of the interference of IEEE 802.11n networks upon IEEE 802.15.4-based WBANs, operating in the 2.4 GHz frequency band. A set of experimental tests were performed, in compliance with the IEEE 802.15.2 standard to assess the coexistence of IEEE 802.15.4 and IEEE 802.11n nodes, operating in overlapping and non-overlapping communication channels. The performed experimental tests considered different distances between the IEEE 802.11n access point and the IEEE 802.15.4 coordinator, as well as different communication channels, and different IEEE 802.11n network data rates. The experimental results highlight a strong interference between both networks, with a clear impact upon the communication reliability.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"28 1","pages":"388-393"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73932391","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8471927
L. Ribeiro, S. Karnouskos, P. Leitão, J. Barbosa, Martin Hochwallner
The increasing need for more adaptive production environments is a big motivator for the adoption of agentbased technologies in industrial systems, as they provide better mechanisms for handling dynamically and intelligently various kinds of production disturbances. Unlike with the utilization of most conventional automation languages, the use of agents enables, in an easy way, the setup of dynamic and autonomous adaptive processes to handle large and complex engineering system functions and interactions. Agent-technologies in cyberphysical systems contexts require at some point integration with automation controllers. However, most commonly available and used agent system implementations in the industry were not designed for hard real-time control use cases, and do not utilize real-time operating systems or dedicated hardware. Hence, they cannot match the hard-real-time performance of automation controllers. This work provides some insights on the performance that can be achieved with agent-based approaches that integrate with low-level automation system functions. It considers the performance of the agent-based practices in light of non-real-time dedicated hardware or operating systems. The results show that agents are well suited for the majority of soft-real-time control applications.
{"title":"Performance Assessment Of The Integration Between Industrial Agents And Low-Level Automation Functions","authors":"L. Ribeiro, S. Karnouskos, P. Leitão, J. Barbosa, Martin Hochwallner","doi":"10.1109/INDIN.2018.8471927","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8471927","url":null,"abstract":"The increasing need for more adaptive production environments is a big motivator for the adoption of agentbased technologies in industrial systems, as they provide better mechanisms for handling dynamically and intelligently various kinds of production disturbances. Unlike with the utilization of most conventional automation languages, the use of agents enables, in an easy way, the setup of dynamic and autonomous adaptive processes to handle large and complex engineering system functions and interactions. Agent-technologies in cyberphysical systems contexts require at some point integration with automation controllers. However, most commonly available and used agent system implementations in the industry were not designed for hard real-time control use cases, and do not utilize real-time operating systems or dedicated hardware. Hence, they cannot match the hard-real-time performance of automation controllers. This work provides some insights on the performance that can be achieved with agent-based approaches that integrate with low-level automation system functions. It considers the performance of the agent-based practices in light of non-real-time dedicated hardware or operating systems. The results show that agents are well suited for the majority of soft-real-time control applications.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"52 1","pages":"121-126"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87224516","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8471968
V. Monteiro, T. Sousa, J. Afonso, J. Afonso
This paper presents an innovative off-board electric vehicle home charging station (EV-HCS) operating as a smart home (SH) enabler. The present status and the proposed perspectives in terms of operation modes are comprehensively addressed along the paper showing the contextualization of the addressed research topic. Comparing with the existing solution, the main motivations and advantages of the off-board EV-HCS are: (a) Off-board dc EV charger, faster than a classical on-board EV charger; (b) Flexible operating power value, aiming an optimized power management in the home; (c) Operation as an active conditioner for the home or the grid, with or without an EV plugged-in, which represents an attractive functionality for enhancing the operation of SHs and smart grids; (d) Bidirectional operation with an EV. The methods used to describe these advantages are validated using computer simulations. The control algorithm is succinctly described, demonstrating its adaptability to the power electronics topology presented for the EV-HCS hardware. The obtained results demonstrate that the proposed EV-HCS presents attractive functionalities for enhancing the EV integration into SHs and smart grids.
{"title":"Innovative Off-Board EV Home Charging Station as a Smart Home Enabler: Present and Proposed Perspectives","authors":"V. Monteiro, T. Sousa, J. Afonso, J. Afonso","doi":"10.1109/INDIN.2018.8471968","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8471968","url":null,"abstract":"This paper presents an innovative off-board electric vehicle home charging station (EV-HCS) operating as a smart home (SH) enabler. The present status and the proposed perspectives in terms of operation modes are comprehensively addressed along the paper showing the contextualization of the addressed research topic. Comparing with the existing solution, the main motivations and advantages of the off-board EV-HCS are: (a) Off-board dc EV charger, faster than a classical on-board EV charger; (b) Flexible operating power value, aiming an optimized power management in the home; (c) Operation as an active conditioner for the home or the grid, with or without an EV plugged-in, which represents an attractive functionality for enhancing the operation of SHs and smart grids; (d) Bidirectional operation with an EV. The methods used to describe these advantages are validated using computer simulations. The control algorithm is succinctly described, demonstrating its adaptability to the power electronics topology presented for the EV-HCS hardware. The obtained results demonstrate that the proposed EV-HCS presents attractive functionalities for enhancing the EV integration into SHs and smart grids.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"90 3 1","pages":"966-971"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85254780","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8472071
Sandeep Patil, Dmitrii Drozdov, V. Vyatkin
Design patterns in software engineering is a generic solution provided for repeatable problems occurring frequently in a software design. They are used a lot in the field of software engineering, especially for object-oriented software development. Different standards exist for design and development of industrial cyber-physical systems and the IEC 61499 standard is one of them. The standard presents a reference component architecture for design and development of distributed industrial cyberphysical systems. There is a lack of design patterns for application development with IEC 61499 standard and this paper address this by proposing some patterns. The design patterns presented are inspired by popular design patterns used in software engineering.
{"title":"Adapting Software Design Patterns To Develop Reusable IEC 61499 Function Block Applications","authors":"Sandeep Patil, Dmitrii Drozdov, V. Vyatkin","doi":"10.1109/INDIN.2018.8472071","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8472071","url":null,"abstract":"Design patterns in software engineering is a generic solution provided for repeatable problems occurring frequently in a software design. They are used a lot in the field of software engineering, especially for object-oriented software development. Different standards exist for design and development of industrial cyber-physical systems and the IEC 61499 standard is one of them. The standard presents a reference component architecture for design and development of distributed industrial cyberphysical systems. There is a lack of design patterns for application development with IEC 61499 standard and this paper address this by proposing some patterns. The design patterns presented are inspired by popular design patterns used in software engineering.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"134 23 1","pages":"725-732"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86484663","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8471934
Zeqing Jiang, W. Dai, Wenjin Wang, Wen Wang
Information and communication technologies (ICT) have shown its impact on medical research over last few years. Big data analysis is adopted in many medical research applications including rehabilitation after surgery. In the facial paralysis rehabilitation training progresses, traditional training processes require huge efforts from both patients and doctors. With increasing number of patients and limited resources offered by hospitals, assistance from ICT are urgently needed. In this paper, we present a cloud-based training and analysis system for facial paralysis patients and physicians that provides rehabilitation training, automatic progress review and result evaluation. A training client is developed to provide rehabilitation training as well as data collection. In addition, training results are analyzed by the cloud platform using machine learning methodologies. The cloud platform provides the automatic evaluation of rehabilitation progresses based on both feedback from training data set and input from physicians.
{"title":"A Cloud-Based Training And Evaluation System For Facial Paralysis Rehabilitation","authors":"Zeqing Jiang, W. Dai, Wenjin Wang, Wen Wang","doi":"10.1109/INDIN.2018.8471934","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8471934","url":null,"abstract":"Information and communication technologies (ICT) have shown its impact on medical research over last few years. Big data analysis is adopted in many medical research applications including rehabilitation after surgery. In the facial paralysis rehabilitation training progresses, traditional training processes require huge efforts from both patients and doctors. With increasing number of patients and limited resources offered by hospitals, assistance from ICT are urgently needed. In this paper, we present a cloud-based training and analysis system for facial paralysis patients and physicians that provides rehabilitation training, automatic progress review and result evaluation. A training client is developed to provide rehabilitation training as well as data collection. In addition, training results are analyzed by the cloud platform using machine learning methodologies. The cloud platform provides the automatic evaluation of rehabilitation progresses based on both feedback from training data set and input from physicians.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"68 1","pages":"701-706"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85099477","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 : 2018-07-01DOI: 10.1109/indin.2018.8472074
{"title":"INDIN 2018 List Reviewer Page","authors":"","doi":"10.1109/indin.2018.8472074","DOIUrl":"https://doi.org/10.1109/indin.2018.8472074","url":null,"abstract":"","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91223287","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8472004
Suhyun Cha, Q. Dong, B. Vogel‐Heuser
Technical debt (TD) describes the long-term negative effect of choosing sub-optimal solutions to achieve a short-term benefit (e.g., cost saving). Engineers from various disciplines, such as mechanical, electrical and software, develop and maintain automated production systems (aPS). Over their development and maintenance period, changes in one discipline might have negative or unexpected impact on the other disciplines due to the interwoven dependencies among the disciplines. Manually derived task list might omit some necessary tasks and, therefore, TD is quite often introduced unintentionally. Also, the interest of TD due to insufficient knowledge sometimes exceeds the short-term cost saving. In this paper, a workflow is proposed to assist in the solution selection process for the aPS domain. The workflow includes the systematic change effort estimation tool considering multidiscipline and TD interest estimation. KAMP4aPS is selected as a tool to estimate the change effort by deriving a fine-grained task list for a modification. Based on such detailed estimation and additional cost factors, one can foresee TD and choose appropriate solution, which fits the current business context. An exemplary change scenario in a lab size plant is presented to demonstrate the suggested methodology.
{"title":"Preventing Technical Debt For Automated Production System Maintenance Using Systematic Change Effort Estimation With Considering Contingent Cost","authors":"Suhyun Cha, Q. Dong, B. Vogel‐Heuser","doi":"10.1109/INDIN.2018.8472004","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8472004","url":null,"abstract":"Technical debt (TD) describes the long-term negative effect of choosing sub-optimal solutions to achieve a short-term benefit (e.g., cost saving). Engineers from various disciplines, such as mechanical, electrical and software, develop and maintain automated production systems (aPS). Over their development and maintenance period, changes in one discipline might have negative or unexpected impact on the other disciplines due to the interwoven dependencies among the disciplines. Manually derived task list might omit some necessary tasks and, therefore, TD is quite often introduced unintentionally. Also, the interest of TD due to insufficient knowledge sometimes exceeds the short-term cost saving. In this paper, a workflow is proposed to assist in the solution selection process for the aPS domain. The workflow includes the systematic change effort estimation tool considering multidiscipline and TD interest estimation. KAMP4aPS is selected as a tool to estimate the change effort by deriving a fine-grained task list for a modification. Based on such detailed estimation and additional cost factors, one can foresee TD and choose appropriate solution, which fits the current business context. An exemplary change scenario in a lab size plant is presented to demonstrate the suggested methodology.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"6 1","pages":"595-601"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81305597","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8472054
Franka Schuster, F. Kopp, A. Paul, H. König
In the course of industrial digitalization, the security of process control networks and especially critical infrastructures has become a major issue that requires novel methods to achieve a multi-level protection. An important feature of this protection is a protocol-specific monitoring within the process control networks that identifies faults and attacks which already have overcome the firewall protection. For a wide-spread application in various sites, this monitoring must be self-adaptive to the different traffic characteristics of the respective networks. Protocol knowledge combined with unsupervised machine learning algorithms can leverage this task. In this paper we present the latest results of applying two machine learning methods on real-world traffic datasets from two plant process control networks. The results for different mappings of the considered packet features are discussed in terms of f-score, precision, and recall. They demonstrate the high potential of using unsupervised learning for training anomaly detectors to identify intrusions in industrial networks.
{"title":"Attack and Fault Detection in Process Control Communication Using Unsupervised Machine Learning","authors":"Franka Schuster, F. Kopp, A. Paul, H. König","doi":"10.1109/INDIN.2018.8472054","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8472054","url":null,"abstract":"In the course of industrial digitalization, the security of process control networks and especially critical infrastructures has become a major issue that requires novel methods to achieve a multi-level protection. An important feature of this protection is a protocol-specific monitoring within the process control networks that identifies faults and attacks which already have overcome the firewall protection. For a wide-spread application in various sites, this monitoring must be self-adaptive to the different traffic characteristics of the respective networks. Protocol knowledge combined with unsupervised machine learning algorithms can leverage this task. In this paper we present the latest results of applying two machine learning methods on real-world traffic datasets from two plant process control networks. The results for different mappings of the considered packet features are discussed in terms of f-score, precision, and recall. They demonstrate the high potential of using unsupervised learning for training anomaly detectors to identify intrusions in industrial networks.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"94 1","pages":"433-438"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75851587","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8471969
Marie Kiermeier, Sebastian Feld
Root cause analysis (RCA) is a central task for quality assurance in manufacturing plants. By tracing back anomalies to its actual trigger, recurrent misbehavior can be eliminated, which improves the system’s future performance. In self-organizing industrial systems (SOIS), however, where the system adapts its behavior to the current circumstances and requests, new challenges arise for RCA. For example, the system decides dynamically at runtime how to route the work-pieces through the factory. This high degree of freedom of the system causes a state space explosion, which makes it difficult to formalize explicit connections. In addition, there are new dependency relationships resulting from the online decision making process and its influencing factors, which have to be taken into account for RCA. Accordingly, in this paper, we present first of all a taxonomy of possible root causes in such SOIS. Thereby, we focus in particular on possible error sources resulting from the online decision making process. Based on this, corresponding backtracking approaches are presented, whereby automatable and non-automatable procedures are distinguished. The latter becomes relevant in case that a component of the online decision making system is not evaluable automatably due to the state space explosion. To trace back anomalies anyway, we propose here a visual analytics solution. A corresponding proof of concept which implements the necessary functions for an expert-based assessment is presented in this paper.
{"title":"Visual Analytics for Root Cause Analysis in Self-Organizing Industrial Systems","authors":"Marie Kiermeier, Sebastian Feld","doi":"10.1109/INDIN.2018.8471969","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8471969","url":null,"abstract":"Root cause analysis (RCA) is a central task for quality assurance in manufacturing plants. By tracing back anomalies to its actual trigger, recurrent misbehavior can be eliminated, which improves the system’s future performance. In self-organizing industrial systems (SOIS), however, where the system adapts its behavior to the current circumstances and requests, new challenges arise for RCA. For example, the system decides dynamically at runtime how to route the work-pieces through the factory. This high degree of freedom of the system causes a state space explosion, which makes it difficult to formalize explicit connections. In addition, there are new dependency relationships resulting from the online decision making process and its influencing factors, which have to be taken into account for RCA. Accordingly, in this paper, we present first of all a taxonomy of possible root causes in such SOIS. Thereby, we focus in particular on possible error sources resulting from the online decision making process. Based on this, corresponding backtracking approaches are presented, whereby automatable and non-automatable procedures are distinguished. The latter becomes relevant in case that a component of the online decision making system is not evaluable automatably due to the state space explosion. To trace back anomalies anyway, we propose here a visual analytics solution. A corresponding proof of concept which implements the necessary functions for an expert-based assessment is presented in this paper.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"34 1","pages":"315-320"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84483074","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 : 2018-07-01DOI: 10.1109/INDIN.2018.8472007
M. Giorgini, J. Aleotti
Virtual reality (VR) will play an important role in the factory of the future. In this paper, an immersive and interactive VR system is presented for3D visualization of automated guided vehicles (AGVs) moving in a warehouse. The environment model consists of a large scale point cloud obtained through a Terrestrial Laser Scanning (TLS) survey. Realistic AGV animation is achieved thanks to the extraction of an accurate model of the ground. Visualization of AGV safety zones is also supported.Moreover, the system enables real-time collision detection between the 3D vehicle model and the point cloud model of the environment. Collision detection is useful for checking the feasibility of a specified vehicle path. Efficient techniques for dynamic loading of massive point cloud data have been developed to speed up rendering and collision detection. The VR system can be used to assist the design of automated warehouses and to show customers what their future industrial plant would look like.
{"title":"Visualization of AGV in Virtual Reality and Collision Detection with Large Scale Point Clouds","authors":"M. Giorgini, J. Aleotti","doi":"10.1109/INDIN.2018.8472007","DOIUrl":"https://doi.org/10.1109/INDIN.2018.8472007","url":null,"abstract":"Virtual reality (VR) will play an important role in the factory of the future. In this paper, an immersive and interactive VR system is presented for3D visualization of automated guided vehicles (AGVs) moving in a warehouse. The environment model consists of a large scale point cloud obtained through a Terrestrial Laser Scanning (TLS) survey. Realistic AGV animation is achieved thanks to the extraction of an accurate model of the ground. Visualization of AGV safety zones is also supported.Moreover, the system enables real-time collision detection between the 3D vehicle model and the point cloud model of the environment. Collision detection is useful for checking the feasibility of a specified vehicle path. Efficient techniques for dynamic loading of massive point cloud data have been developed to speed up rendering and collision detection. The VR system can be used to assist the design of automated warehouses and to show customers what their future industrial plant would look like.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"59 1","pages":"905-910"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79573322","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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