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  • Periodic Reporting for period 1 - SatisFactory (A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments)

SatisFactory Report Summary

Project ID: 636302
Funded under: H2020-EU.

Periodic Reporting for period 1 - SatisFactory (A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments)

Reporting period: 2015-01-01 to 2016-06-30

Summary of the context and overall objectives of the project

Transfiguration of traditional industrial environments into attractive and safe workplaces is a key to the Industrial Revolution. SatisFactory (“A Collaborative and Augmented-Enabled Ecosystem for Increasing Satisfaction and Working Experience in Smart Factory Environments”) is a three-year long research project funded by the European Commission under the HORIZON2020 programme, which started in January 2015 to tackle this problem.

Manufacturing is vital component of our society, but can only realise its full potential if it can embrace the ongoing changes in global economy and technology. It should be adapted on the new opportunities that are stemming from the development and integration of “factories of things”. Some of the key issues that should be addressed are: the enhancement of industrial infrastructure, the incorporation of intelligent technologies in manufacturing environments, the development of intelligent human-centric technologies but more importantly the assessment and communication of the employee’s expertise, through the stimulation of team interactions. SatisFactory envisions a factory of the future as a healthy place providing pleasant working experience. It is a place where staff members feel appreciated and valued for their contribution and are delighted to come to work every day. This enhancement of the overall working experience in a factory in combination with appropriate marketing and recruiting strategies will make industrial employment more attractive to potential younger applicants as well as enhance the wellbeing and satisfaction of the employees.

SatisFactory aims to enhance and enrich the manufacturing working environment towards attractive factories of the future that encompass key enabling technologies such as augmented reality, wearable and ubiquitous computing as well as and customised social communication platforms coupled with experience design and gamification techniques for the efficient transfer of knowledge and experience among employees. The ultimate goal will be to develop and deploy emerging knowledge-driven training techniques (gamification aware) and wearable devices (AR-enabled Glasses) for the enhancement of innovation, productivity and scheduling of work in the production line while enriching its flexibility through the support of team interactions in the shop floor which consequently will add to the work-related satisfaction. Special attention in the deployment of cutting-edge incentives (wearable computing, gamified working experience) will be given towards further increase of attractiveness to the younger generation. The focus on user experience and gamification will ensure optimal balance between workers performance and satisfaction.
To this end, the SatisFactory framework will be comprised by five major components: (a) The Smart Sensor Network that consists of the heterogeneous devices that will allow the interaction between the physical world and SatisFactory framework. This network will be integrated through Middleware. (b) The Ontology Manager which processes the data collected by the Smart Sensor Network and transform them into semantically enriched events. (c) The integrated Decision Support System which processes the events and semantically enriched data, and is responsible for supporting decision making in manufacturing processes and operations. (d) The Augmented Reality In-Factory platform, which provides additional relevant data to the employee while working, or to reach a realistic experience while being trained in a simulated framework. (e) The training/educational environment, which consists of a set of components that are used for in-factory training and support of the workers. AR technologies are used to deliver “on-the-job” training and education for not only workers and machinery operators but also for manufacturing process supervisors.

In order for the SatisFactory project to successfully reach its goals, several prerequisites are set in the

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the first period of the SatisFactory project, the SatisFactory Consortium has performed significant progress strictly following the project’s Description of Action (DoA –Annex I). The work in the first period of the project (M1-M18) focused on the user requirements analysis and use cases definition, as well as on the overall SatisFactory system design and specification. In parallel, the development efforts have been proceeded in all domains of the SatisFactory project, e.g. collaboration platform, gamification framework, HR re-adaptation toolkit, context-aware managers, decision support engine, IoT infrastructure, AR glasses, AR toolkit and “on-the-job” education toolkit, along with the technical developments of the SatisFactory hardware infrastructure. Moreover, project efforts focused also on several horizontal activities regarding diffusing project objectives, concepts and achievements in key stakeholders as well as to the general public.

Very briefly, the developments for the reporting period can be summarized but are not limited to the following points:

- Definition of end-user, shop-floor and system requirements and specifications
The collection of end-user needs and system requirements and specifications is the fundamental basis for the development of any system or product. Furthermore, the accurate understanding of user requirements is specifically important for the introduction of the solutions in the shop-floor, since they address among others safety and ergonomic issues, training, etc. In this direction, the definition of the requirements has been performed with the support of specific material, like questionnaires and by performing face-to-face meeting and workshops in order to apply as much as possible the concept of the user-centered design. The SatisFactory project involved the participation of 3 shop-floor from 2 different countries (Greece and Italy) and 41 users (18 from CERTH/CPERI, 15 from COMAU, and 8 from SUNLIGHT). All the collected material has been analysed and the most relevant concepts have been extracted and summarized in requirements and specifications. This process is iterative and it is actually in its second iteration out of three. Finally, 89 requirements (functional & non-functional) have been defined so far.

Related Submitted Deliverables: D1.1 “User group definitions, end-user needs, requirement analysis and development guidelines”.

- Actors and procedures interconnection modelling
The core scope of the actor & procedures modelling framework is to identify and create the necessary models for the high-level analysis of the business processes of the industrial domains introduced by Satisfactory project. At a first towards fulfilling this objective, a detailed analysis has been performed for identifying and analyzing existing standards and available tools for eventually selecting the most appropriate modelling framework to be applied in Satisfactory. The performed activities covered the full Business Process Modelling lifecycle (BPM) analysis in order to focus on the modelling standards that can be used in the project for capturing the industrial standard operating procedures. Efforts in this respect concluded with the 1) classification of the procedures (e.g. manufacturing, maintenance, training, etc.) and the 2) identification of specific needs and requirements related to the management of procedures.
Based upon the findings of the survey conducted, a concrete methodology (5-steps) based on multi-actor and multi-criteria approach has been derived for analyzing the actors & procedures of the Satisfactory shop floors. In this context, several groups of actors have been created taking into consideration the role, the level of experience, the knowledge and the responsibilities of each actor. Moreover, detailed interviews with the actors at each shop floor were conducted in order to better analyze the shop floors of Satisfactory. Furthermore, in order to have a unified appro

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

"The key innovation points of the project can be summarized as following:
- AR glasses software, firmware and hardware
One of the key innovation points on GLASSUP’s AR Glasses is that they have been designed for an industrial environment from the beginning. Thus, the protection and the ergonomics of the eyewear are a primary factor and this is the only AR eyewear known based on this philosophy.
Also, one of the activities that will be completed in the next period is the support for a thermal camera that will be shown directly on the device, a sensor that has never been integrated in any AR device available on the market.
Another interesting innovation is based on the fact that we are providing a REST API to connect directly to the eyewear and an SDK for IOS, Android and Windows.

- AR ""on-job""education & training toolkit
The expressive power of the Standard Operating Procedures Model is really a significant plus and it is – no more, not less – a redefinition of the word: procedure as used in an industrial environment. It has the ability to describe arbitrary procedures allowing to distribute them as a digital, versioned, rich-media, consistent bundle that can be downloaded and transmitted in an single transaction. Finally, The capability to incorporate external sensors readings into a live training / working session makes the tool able to test the trainee in real time with ad-hoc settings.

- Algorithms and technology for employees localization
The design and development of the localization algorithm for employees has been done for the UWB-based wearable device presented is section 1.2. Since the wearable device needs to operate in harsh industrial environments, an innovative robust indoor localization algorithm based on a Bayesian method has been designed and developed. In order to make the localization more robust against the numerous interferences, a hybrid and cooperative localization has been designed. As a first step, the designed algorithm was focused on a distributed hybrid algorithm based on both UWB radio technology and inertial sensors. The cooperative approach along with a TDMA method, for avoiding collision between the UWB wearable devices, will be developed in the second half of the project.
Inertial sensors have been adopted to detect the acceleration, the angular speed and the orientation of the moving target by using its sensors (accelerometers, gyroscopes, magnetometers); note that these are additional information that cannot be observable by using only the UWB radio. These observations in combination with the UWB-based ranging measurements improve the localization performance because they add a full knowledge of the tag kinematics. In addition, the inertial sensors are able to provide the localization estimation even when the ranging is not available or the number of measurements is not enough for estimating the position.
The localization algorithm consists of three parts. Part 1 checks if the inertial measurements have to be processed or not. This choice depends on the level of disturbances and if the acceleration of the worker is zero or in static conditions without disturbances. Part 2, which uses the measurements received from part 1, implements an Attitude Heading Reference System (AHRS) that estimates and corrects the errors of the estimated worker’s attitude and the bias of the gyroscope. Finally, part 3 implements an Extended Kalman Filter (EKF) based on a Position-Velocity-Acceleration (PVA) model that performs the data fusion task of the hybrid localization. In particular, it estimates both the location and the bias of the accelerometer taking as input: the estimated and corrected attitude, the acceleration in body frame and the UWB ranging.
In particular, the EKF AHRS and the EKF PVA were developed and tested as separated modules. However, due to some problems related to the available RAM of the wearable device, it was not possible to test the complete designed algorithm; instead of that,"

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