The 10 most recently published documents
Geomagnetic fingerprinting is a promising technology for supplying smartphone indoor navigation algorithms with infrastructureless and to some extent stable local position information. Geomagnetic disturbances in buildings impose a characteristic magnetic signature which can be detected by the phones magnetic sensor. A common approach is to create a database by recording reference fingerprints for example along a predefined known path. In the positioning phase, magnetic data is recorded along a certain time- or path length of the unknown path. The live data can be analyzed and compared against
the prerecorded reference fingerprints. This magnetic matching procedure differs considerably from WiFi fingerprinting, where WiFi data from discrete points is compared. The main difference is that the
described approach uses data recorded as time series. The matching has to consider not only the signal amplitude but also temporal- or spatial matching. In this paper several magnetic matching algorithms are evaluated for usage in an indoor positioning system. A public database is used as data source allowing comparison of the results with other works.
Automatic disassembly planning for complex industrial products like vehicles checks the expandability of components already at early stages of design. For a fast computation of collision-free disassembly paths, sampling-based rigid body motion planning is used in the literature. However, in real-world scenarios there are circumstances that prevent the finding of plausible collision-free disassembly paths with these conventional motion planners. The most difficult problem is that many components have deformable fastening elements that are modeled in a relaxed state and often as a part of the rigid object. The fastening elements cause unavoidable collisions of the component with its environment along the actual disassembly path. In this paper, we present Iterative Mesh Modification Planning (IMMP). Given the information about fastening elements in advance, our method applies a controlled iterative process of geometric deformations and planning attempts to the component to be disassembled. With this process, we are able to disassemble the component from its installed position with a conventional rigid body motion planner taking fastening elements and also overpressure into account. We demonstrate the effectiveness of our method on real-world planning scenarios from the automotive industry.
We present the first realization of an assembly sequence planning framework for large-scale and complex 3D real-world CAD scenarios. Other than in academic benchmark data sets, in our scenario each assembled part is allowed to contain flexible fastening elements and the number of assembled parts is quite high. With our framework we are able to derive a meaningful assembly priority graph for the parts. Our framework divides the disassembly motion of each part into a NEAR- and a subsequent FAR planning phase and uses existing specialized motion planners for each phase. To reduce the number of unsuccessful motion planning requests we use a general Voronoi diagram graph and a novel collision perceiving method which significantly speed up our framework. At the end, we create an assembly priority graph to indicate which parts must be disassembled before others. In our experiments, we show that our framework is the first one which is able to generate a priority graph for a representative data set from the automotive industry. Moreover, the reported disassembly motions for the individual parts are shorter and can be computed faster than with other state-of-the-art frameworks.
The automatic generation of (dis)assembly sequences for complex technical products is a challenging field. Complex products like vehicles consist of numerous different components. Determining the sequence using a brute-force-approach by testing all components for disassembly one after another in a loop until all components are disassembled is laborious and costly. In industrial scenarios, a large proportion of the components are fasteners. In this paper, we propose a new framework which improves the disassembly sequencing generation by prioritizing fasteners during planning. Our proposed framework comprises a preprocessing in which fasteners are identified with a convolutional neural network within a dataset and a procedure that preferentially and automatically checks fasteners for disassembly. The algorithm takes initial and unavoidable collisions of the fasteners into account. We show the effectiveness of our approach on real-world data from the automotive industry. A new synthetic dataset of fasteners for training neural networks is available.
Online labs form the basis of digital exchange in networks and are thus candidates for the use of shared knowledge, shared infrastructure, and shared facilities through the application of ICT technology. In addition to technical and didactic considerations, the importance of organizational considerations in this respect is increasing due to shared use. In this paper, the organizational foundations of digital sharing are highlighted, providing a long-term perspective on lab networks. To this end, three organizational aspects are addressed: (1) a platform business model for activating online lab
sharing, (2) considerations on building initial and long-term trust between actors as a critical challenge of a lab sharing platform, and (3) a maturity model for capturing the organizational transformation of online labs for platform actors. Using the case study DigiLab4U, a time-limited, funded research project on online lab sharing, this paper shows how the three organizational considerations can contribute to sustainability over the funding period. The reader is thereby shown which success criteria and functional requirements are necessary for the sustainability of a lab-sharing network.
In this chapter, a research-oriented lecture on RFID application in logistics is provided. The chapter explains the theoretical background of UHF RFID and is driven by a user story, based on an industry-like scenario in a food & beverages supply chain. Indeed, the second section of this chapter will deep dive into the user story, allowing the reader to personify a supply chain management consultant into a pilot implementation of RFID technologies in the food & beverages sector. Also, the lecture presented in this chapter is strictly connected with practical lab experience. By means of QR codes,
in fact, readers might be linked to supplementary resources, such as data repositories or even experiments, performed either in batch (i.e. readers specify the experimental settings, and then the experiment is performed and the results are provided at a later time) or remotely (i.e. readers become experimenters by remotely accessing and operating lab resources). When carrying out the lab experience, readers are encouraged to work in groups of 2–4 people to set and/or perform the experiments, and to analyze experimental results. The aim of this group work is to foster cooperative learning, especially when this chapter is adopted in higher or vocational education classes or courses.
Industry 4.0, the Industrial Internet of Things (IIoT) as well as Smart Logistics depend on locating mobile assets. In contrast to outdoor locating, GPS is not reliable for indoor positioning. Instead, different real-time locating systems (RTLS) are used in industries for indoor locating when there is no chance of obtaining GPS-satellite signals. Students in engineering disciplines should know about the chances offered by and the limits of RTLS, for example through corresponding lab experiments. However, measuring the accuracy of RTLS is a time-consuming task. Our goal is to provide a remote RTLS-accuracy measurement experiment by digitalizing and automating the whole process.
This paper discusses adding remote experiment service to this lab, thus providing access to the lab infrastructure anytime and anywhere. A mobile robot was used to move an ultra-wideband (UWB) transponder and expose it to the RTLS measurement infrastructure. By optimizing the routing algorithm of a mobile robot, the required accuracy and appropriate safety features were justified and the accuracy of the robot reached 2 cm. It also passed all the static and dynamic obstacles with acceptable safety thanks to inbuilt sensors. The remote operation was also done in an IoT environment by implementing the MQTT data transfer protocol. For remote users to be able to operate our RTLS system via MQTT, we developed a software program. When running this program, our DigiLab4U Laboratory Management System (LabMS) is able to send commands to the RTLS system and receive positioning measurements of a mobile object (in our case RTLS tag) via MQTT messages. Thus, the real route and the measured route can be compared and the difference can be analyzed by students remotely.
Comparing Service-Oriented System Management Solutions in Remote and Virtual Laboratory Environments
(2022)
Digitalized laboratories are gaining importance in the higher education sector. Students are being provided with remote access to physical laboratory infrastructures as well as online access to virtual labs. Due to the complexity of systems in digital laboratory environments, it is often difficult to manage the applications efficiently. Moreover, there can be multiple types of labora
tories with different system configurations. These laboratories need different management solutions based on the heterogeneity of lab systems. Therefore, different approaches are needed to create deployable software units which support multiple architectures.
We compare a microservices approach and monolithic architectures. As
regards production deployment, virtualization and containerization along with their benefits and disadvantages are considered. In our research, we compared Docker solutions as well as the main Kubernetes tools like Minikube, Kubeadm, K3S, and Microk8s. Our goal is to identify solutions that are easy to manage even in heterogeneous hardware environments. Security, high availability, and compatibility with digitalized laboratories are also considered.
For intelligent mobility concepts in growing urban environments, positioning of transportation vehicles and generally moving objects is a fundamental prerequisite. Global Navigation Satellite Systems (GNSS) are commonly used for this purpose, but especially in urban environments under certain conditions, they offer limited accuracy due to buildings, tunnels, etc. that can deviate or mask the satellite signals. The use of existing built-in sensors of the vehicle and the installation of additional sensors can be utilized to describe the movement of the vehicle independently of GNSS. This conforms to the concept of dead reckoning (DR). Both systems (GNSS and DR) can be integrated and prepared to work together since they compensate their respective weaknesses efficiently. In this study, a method to integrate different inertial sensors (gyroscope and accelerometer) and GNSS is investigated. Pedelecs usually do not have many inbuilt additional sensors like it is the case in cars; therefore, additional low-cost sensors have to be used. An extended Kalman filter (EKF) is the base of calculations to perform data integration. Driving tests are realized to check the performance of the integration model. The results show that positioning in situations where GNSS data is not available can be done through dead reckoning for a short period of time. The weak point hereby is the calibration of the accelerometer. Inaccurate accelerometer data cause increasing inaccuracy of the position due to the double integration of the acceleration over time.
There is increased activity in developing workflows and implementations in the context of urban energy analysis simulation based on 3D city models in smart cities. At the University of Applied Sciences Stuttgart (HFT Stuttgart), an urban energy simulation platform called ‘SimStadt’ has successfully been developed. It uses the CityGML 3D city model to simulate the heat demand, photovoltaic potential, and other scenarios that provide dynamic simulation results in both space and time dimensions. Accordingly, a tool for managing dynamic data of the CityGML models is required. Earlier, the CityGML Application Domain Extension (ADE) had been proposed to support additional attributes of the CityGML model; however, there is still a lack of open-source tools and platforms to manage and distribute the CityGML ADE data efficiently. This article evaluates and compares alternative methods to manage dynamic simulation results of the 3D city model and visualise these data on the 3D web-based smart city application, including the use of SimStadt web services, databases, and OGC SensorThings API standard.
