Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing

Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies

Bibliometric analysis of the emerging phenomenon of smart factories

Research Question (RQ): Bibliometric studies provide a useful tool in reviewing scientific research, by using quantitative methods for analyzing all available publications in a research area of interest, in our case research on smart factories. Therefore, the following research questions occurred: 1. how much research has been done on smart factories, since the concept first appeared in 2011? 2. what characterizes the available publications? Purpose: The purpose of our study is to analyze the extent of the available literature on the topic of smart factories, along with classifying the characteristics of available contributions, namely journal papers, conference papers and book chapter, along with their impact indicators. Method: Bibliometric analysis and historical literature review was done with the help of the Clarivate Analytics Web of Science bases: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, and IC. Results: We found that there are a total of 123 contributions to the field of smart factory research, from 2011 till 2017, and that most of these contributions fall under engineering and other technology related research areas, while a few fall within the social science category. Organization: Our study can help traditional factories and emerging smart factories learn about developments in the field of new smart technologies and learn information that might help them change their business models. Society: The number of citations helps determine the impact a paper or set of papers has had on a particular field of research or science in general, which can help other authors determine which papers might be useful for their own research. Originality: Up-to-date bibliometric analysis of Web of Science literature in the field of smart factories. Limitations / further research: Bibliometric studies only provide information on whether or not other authors found particular publications useful and do not provide information on why the publications were useful to those authors. Bibliometric studies thus serve a descriptive role and not a prescriptive role

An Iterative Approach for Collision Feee Routing and Scheduling in Multirobot Stations

This work is inspired by the problem of planning sequences of operations, as welding, in car manufacturing stations where multiple industrial robots cooperate. The goal is to minimize the station cycle time, \emph{i.e.} the time it takes for the last robot to finish its cycle. This is done by dispatching the tasks among the robots, and by routing and scheduling the robots in a collision-free way, such that they perform all predefined tasks. We propose an iterative and decoupled approach in order to cope with the high complexity of the problem. First, collisions among robots are neglected, leading to a min-max Multiple Generalized Traveling Salesman Problem (MGTSP). Then, when the sets of robot loads have been obtained and fixed, we sequence and schedule their tasks, with the aim to avoid conflicts. The first problem (min-max MGTSP) is solved by an exact branch and bound method, where different lower bounds are presented by combining the solutions of a min-max set partitioning problem and of a Generalized Traveling Salesman Problem (GTSP). The second problem is approached by assuming that robots move synchronously: a novel transformation of this synchronous problem into a GTSP is presented. Eventually, in order to provide complete robot solutions, we include path planning functionalities, allowing the robots to avoid collisions with the static environment and among themselves. These steps are iterated until a satisfying solution is obtained. Experimental results are shown for both problems and for their combination. We even show the results of the iterative method, applied to an industrial test case adapted from a stud welding station in a car manufacturing line

Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing

Today, ubiquitously sensing technologies enable inter-connection of physical\ua0objects, as part of Internet of Things (IoT), and provide massive amounts of\ua0data streams. In such scenarios, the demand for timely analysis has resulted in\ua0a shift of data processing paradigms towards continuous, parallel, and multitier\ua0computing. However, these paradigms are followed by several challenges\ua0especially regarding analysis speed, precision, costs, and deterministic execution.\ua0This thesis studies a number of such challenges to enable efficient continuous\ua0processing of streams of data in a decentralized and timely manner.In the first part of the thesis, we investigate techniques aiming at speeding\ua0up the processing without a loss in precision. The focus is on continuous\ua0machine learning/data mining types of problems, appearing commonly in IoT\ua0applications, and in particular continuous clustering and monitoring, for which\ua0we present novel algorithms; (i) Lisco, a sequential algorithm to cluster data\ua0points collected by LiDAR (a distance sensor that creates a 3D mapping of the\ua0environment), (ii) p-Lisco, the parallel version of Lisco to enhance pipeline- and\ua0data-parallelism of the latter, (iii) pi-Lisco, the parallel and incremental version\ua0to reuse the information and prevent redundant computations, (iv) g-Lisco, a\ua0generalized version of Lisco to cluster any data with spatio-temporal locality\ua0by leveraging the implicit ordering of the data, and (v) Amble, a continuous\ua0monitoring solution in an industrial process.In the second part, we investigate techniques to reduce the analysis costs\ua0in addition to speeding up the processing while also supporting deterministic\ua0execution. The focus is on problems associated with availability and utilization\ua0of computing resources, namely reducing the volumes of data, involving\ua0concurrent computing elements, and adjusting the level of concurrency. For\ua0that, we propose three frameworks; (i) DRIVEN, a framework to continuously\ua0compress the data and enable efficient transmission of the compact data in the\ua0processing pipeline, (ii) STRATUM, a framework to continuously pre-process\ua0the data before transferring the later to upper tiers for further processing, and\ua0(iii) STRETCH, a framework to enable instantaneous elastic reconfigurations\ua0to adjust intra-node resources at runtime while ensuring determinism.The algorithms and frameworks presented in this thesis contribute to an\ua0efficient processing of data streams in an online manner while utilizing available\ua0resources. Using extensive evaluations, we show the efficiency and achievements\ua0of the proposed techniques for IoT representative applications that involve a\ua0wide spectrum of platforms, and illustrate that the performance of our work\ua0exceeds that of state-of-the-art techniques

A Framework for Systematic use of Realistic Visualisation to Support Layout Planning of Production Systems

The process of designing production systems comprises a sequence of steps toward the final design and realisation. Layout planning is a significant part of this process. Its outcome should be a layout which matches the existing spatial conditions of the factory building and desired performance of the production system. To support layout planning, virtual representations of layouts can be created to plan and evaluate layout alternatives. Costly problems can arise during the realisation, if the virtual representations are inaccurate or lack details of the factory building and planned production systems. 3D laser scanning can be used to create accurate and detailed virtual representations by capturing the spatial conditions of existing factory buildings. The data from a 3D laser scan can be used for realistic visualisation of the existing factory building. If this is combined with 3D CAD models of new equipment, the planned production system layout can also be visualised realistically. Realistic visualisation has been shown to enable accurate planning and evaluation of production system layouts, but it does require a systematic working method.The aim of this thesis is to outline and evaluate a framework for systematic use of realistic visualisation to support layout planning of production systems. This aim is addressed using an action research design; this incorporates five industrial studies targeting industrial projects designing production systems. The framework is outlined and evaluated based on the results of the industrial studies.The outlined framework follows a project model for production systems design. It includes several design activities which rely on realistic visualisation of the planned production system layouts. The framework can be used to support the layout planning of industrial projects designing production systems. Its outcomes include making the correct decisions, reducing costly risks and problems and reducing overall project time. Layout planning supported by realistic visualisation allows manufacturing companies to reduce uncertainty when realising planned production systems

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

Domain Computing: The Next Generation of Computing

Computers are indispensable in our daily lives. The first generation of computing started the era of human automation computing. These machine’s computational resources, however, were completely centralized in local machines. With the appearance of networks, the second generation of computing significantly improved data availability and portability so that computing resources could be efficiently shared among the networks. The service-oriented third generation of computing provided functionality by breaking down applications into services, on-demand computing through utility and cloud infrastructures, as well as ubiquitous accesses from wide-spread geographical networks. Services as primary computing resources are far spread from lo- cal to worldwide. These services loosely couple applications and servers, which allows services to scale up easily with higher availability. The complexity of locating, utilizing and optimizing computational resources becomes even more challenging as these resources become more available, fault-tolerant, scalable, better per- forming, and spatially distributed. The critical question becomes how do applications dynamically utilize and optimize unique/duplicate/competitive resources at runtime in the most efficient and effective way without code changes, as well as providing high available, scalable, secured and easy development services. Domain computing proposes a new way to manage computational resources and applications. Domain computing dy- namically manages resources within logic entities, domains, and without being bound to physical machines so that application functionality can be extended at runtime. Moreover, domain computing introduces domains as a replacement of a traditional computer in order to run applications and link different computational resources that are distributed over networks into domains so that a user can greatly improve and optimize the resource utilization at a global level. By negotiating with different layers, domain computing dynamically links different resources, shares resources and cooperates with domains at runtime so applications can more quickly adapt to dynamically changing environments and gain better performance. Also, domain computing presents a new way to develop applications which are resource stateless based. In this work, a prototype sys- tem was built and the performance of its various aspects has been examined, including network throughput, response time, variance, resource publishing and subscription, and secured communications

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Clustering in the Big Data Era: methods for efficient approximation, distribution, and parallelization

Data clustering is an unsupervised machine learning task whose objective is to group together similar items. As a versatile data mining tool, data clustering has numerous applications, such as object detection and localization using data from 3D laser-based sensors, finding popular routes using geolocation data, and finding similar patterns of electricity consumption using smart meters.The datasets in modern IoT-based applications are getting more and more challenging for conventional clustering schemes. Big Data is a term used to loosely describe hard-to-manage datasets. Particularly, large numbers of data points, high rates of data production, large numbers of dimensions, high skewness, and distributed data sources are aspects that challenge the classical data processing schemes, including clustering methods. This thesis contributes to efficient big data clustering for distributed and parallel computing architectures, representative of the processing environments in edge-cloud computing continuum. The thesis also proposes approximation techniques to cope with certain challenging aspects of big data.Regarding distributed clustering, the thesis proposes MAD-C, abbreviating Multi-stage Approximate Distributed Cluster-Combining. MAD-C leverages an approximation-based data synopsis that drastically lowers the required communication bandwidth among the distributed nodes and achieves multiplicative savings in computation time, compared to a baseline that centrally gathers and clusters the data. The thesis shows MAD-C can be used to detect and localize objects using data from distributed 3D laser-based sensors with high accuracy. Furthermore, the work in the thesis shows how to utilize MAD-C to efficiently detect the objects within a restricted area for geofencing purposes.Regarding parallel clustering, the thesis proposes a family of algorithms called PARMA-CC, abbreviating Parallel Multistage Approximate Cluster Combining. Using approximation-based data synopsis, PARMA-CC algorithms achieve scalability on multi-core systems by facilitating parallel execution of threads with limited dependencies which get resolved using fine-grained synchronization techniques. To further enhance the efficiency, PARMA-CC algorithms can be configured with respect to different data properties. Analytical and empirical evaluations show PARMA-CC algorithms achieve significantly higher scalability than the state-of-the-art methods while preserving a high accuracy.On parallel high dimensional clustering, the thesis proposes IP.LSH.DBSCAN, abbreviating Integrated Parallel Density-Based Clustering through Locality-Sensitive Hashing (LSH). IP.LSH.DBSCAN fuses the process of creating an LSH index into the process of data clustering, and it takes advantage of data parallelization and fine-grained synchronization. Analytical and empirical evaluations show IP.LSH.DBSCAN facilitates parallel density-based clustering of massive datasets using desired distance measures resulting in several orders of magnitude lower latency than state-of-the-art for high dimensional data.In essence, the thesis proposes methods and algorithmic implementations targeting the problem of big data clustering and applications using distributed and parallel processing. The proposed methods (available as open source software) are extensible and can be used in combination with other methods