Traditional IMU based PDR systems suffer from rapidly growing drift effects due to the inherent bias of the inertial sensor. Many existing solutions to mitigate this problem use aiding sensors or information as heuristics or map data. We propose a new optimization framework to solve the PDR estimation problem where the sensors biases are explicitly included as state variables and therefore be used to correct for bias effects in the PDR. By using a smoothing approach and exploiting the rigid structure of a MIMU array one can solve for the slowly varying sensor biases. This paper presents the method and gives an exemplary result of a walking trial. Good agreements in the position and orientation with an optical reference system were found. Moreover, accelerometer and gyroscope biases could be estimated accordingly. Further research includes the performance of more experiments under various conditions such that a more quantitative evaluation can be obtained. In addition, an exploration of a (pseudo) realtime filter version would be valuable such that the system can be applied online.
MULTIFILE
Within the profile Technical Information Technology (ICT Department) the most important specializations are Embedded Software and Industrial Automation. About half of the Technical Information curriculum consists of learning modules, the other half is organized in projects. The whole study lasts four years. After two-and-a-half year students choose a specialization. Before the choice is made students have several occasions in which they learn something about the possible fields of specialization. In the first and second year there are two modules about Industrial Automation. First there is a module on actuators, sensors and interfacing, later a module on production systems. Finally there is an Industrial Automation project. In this project groups of students get the assignment to develop the control for a scale model flexible automation cell or to develop a monitoring system for this cell. In the last year of their studies students participate in a larger Industrial Automation project, often with an assignment from Industry. Here also the possibility exists to join multidisciplinary projects (IPD; integrated product development).
Author supplied: Abstract—The growing importance and impact of new technologies are changing many industries. This effect is especially noticeable in the manufacturing industry. This paper explores a practical implementation of a hybrid architecture for the newest generation of manufacturing systems. The papers starts with a proposition that envisions reconfigurable systems that work together autonomously to create Manufacturing as a Service (MaaS). It introduces a number of problems in this area and shows the requirements for an architecture that can be the main research platform to solve a number of these problems, including the need for safe and flexible system behaviour and the ability to reconfigure with limited interference to other systems within the manufacturing environment. The paper highlights the infrastructure and architecture itself that can support the requirements to solve the mentioned problems in the future. A concept system named Grid Manufacturing is then introduced that shows both the hardware and software systems to handle the challenges. The paper then moves towards the design of the architecture and introduces all systems involved, including the specific hardware platforms that will be controlled by the software platform called REXOS (Reconfigurable EQuipletS Operating System). The design choices are provided that show why it has become a hybrid platform that uses Java Agent Development Framework (JADE) and Robot Operating System (ROS). Finally, to validate REXOS, the performance is measured and discussed, which shows that REXOS can be used as a practical basis for more specific research for robust autonomous reconfigurable systems and application in industry 4.0. This paper shows practical examples of how to successfully combine several technologies that are meant to lead to a faster adoption and a better business case for autonomous and reconfigurable systems in industry.
In het project Flexible Manufacturing onderzoeken we hoe generieke industriële robots optimaal gebruikt kunnen worden in het bedrijfsleven. Het gaat daarbij om de inzet van software en ICT bij robots om de productie te optimaliseren. Op dit moment worden robots voornamelijk ingezet voor relatief eenvoudig, gestructureerd en repetitief werk. In combinatie met verschillende sensorensystemen kunnen robots echter juist hele complexe taken uitvoeren waarbij de robots zich aanpassen aan de situatie. Een mogelijke verklaring voor bovenstaande situatie is dat robots niet gemakkelijk en snel genoeg kunnen worden ge(her)programmeerd, gecombineerd met verschillende sensorsystemen en gecombineerd met andere softwarepakketten om ze nieuwe taken te laten uitvoeren. In het voortraject van dit projectvoorstel kwam een opvallend verschil tussen de werkwijze op enerzijds hogescholen en universiteiten en anderzijds het bedrijfsleven naar voren. In het bedrijfsleven gebruikt men hoofdzakelijk de commerciële en merkspecifieke software van de robotfabrikanten. Op hogescholen en universiteiten wordt daarentegen hoofdzakelijk gebruik gemaakt van open-source en generieke ontwikkelframeworks. Het framework dat wereldwijd het meest wordt gebruikt is Robot Operating System, oftewel ROS. In dit project willen we meer inzicht krijgen in deze twee verschillende benaderingen. Daarvoor analyseren we de bestaande commerciële software voor de grote merken van robotarmen en vergelijken deze met ROS. Naast een studie naar de overeenkomsten en verschillen wordt binnen het project een grote bijeenkomst georganiseerd met bedrijven uit de regio om te achterhalen welke overwegingen bedrijven maken bij het toepassen van robots en welke hindernissen ze ondervinden bij het daadwerkelijke gebruik van robots. Deze aanvraag wordt parallel gedaan met de Raak KIEM Smart Industry aanvraag: Twentse ROS. Het doel van de huidige aanvraag is om tot de kern van het probleem te komen: waarom worden robots nog niet optimaal ingezet in industrie? Het doel is om op basis van beide KIEM aanvragen een Raak PRO te ontwikkelen op dit onderwerp.
What if living organisms communicated signals from the environment to us and thereby offered a sustainable alternative to electronic sensors? Within the field of biodesign, designers and scientists are collaborating with living organisms to produce new materials with ecological benefits. The company Hoekmine, in collaboration with designers, has been researching the potential of flavobacteria for producing sustainable colorants to be applied on everyday products. These non-harmful bacteria can change their form, texture and iridescent color in response to diverse environmental factors, such as humidity and temperature. Here, billions of cells are sensing and integrating the results as color. Therefore, Hoekmine envisions biosensors, which would minimize the use of increasingly demanded electronic sensors, and thus, the implementation of scarce and toxic materials. Developing a living sensor by hosting flavobacteria in a biobased and biodegradable flexible material offers opportunities for sustainable alternatives to electronic sensors. Aiming to take this concept to the next level, we propose a research collaboration between Avans, Hoekmine and a company specialized in biobased and biodegradable labels, Bio4Life. Together with this interdisciplinary team, we aim to bridge microbiology and embodiment design, and contribute to the development of a circular economy where digital technology and organic systems merge in the design of Living Circular Labels (LCLs). Throughout the project we will use an iterative approach between designing and testing LCLs that host living flavobacteria and additionally, methods for the end user to activate the bacteria’s growth at a given time.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
