Guide ⋆ SHOP4CF

Guía de solicitantes

Cierre de recepción de solicitudes

2 de Enero del 2020 a las 15h CET

INTRODUCCIÓN

UNICEF es el Fondo de Naciones Unidas para la Infancia, que tiene como misión luchar por el bienestar y los Derechos de la infancia en todo el mundo. UNICEF articula su presencia en países industrializados a través de Comités Nacionales, siendo la Fundación UNICEF Comité Español la representación de UNICEF en nuestro país, cuyo objetivo es sensibilizar y comunicar a la sociedad española los objetivos, prioridades y acciones de UNICEF en todo el mundo, y captar fondos en apoyo a las mismas.

Para desarrollar su misión, ha creado UNICEF Lab, una iniciativa cuyo principal objetivo es encontrar soluciones innovadoras a los desafíos que enfrenta la infancia más vulnerable.

En pos de conseguir este objetivo ha alcanzado un acuerdo de colaboración con ISDI Accelerator, que se ha comprometido, entre otras actuaciones, a desarrollar un programa de aceleración de Startups de carácter social con foco en los Derechos de Infancia.

El programa de aceleración desarrollado por ISDI Accelerator apoyará un máximo de 5 startups con hasta 15.000€ por compañía más formación, mentoring y un DemoDay delante de responsables de UNICEF Comité Español e inversores privados.

Por favor, dirígete a la página oficial https://www.accelerator.isdi.education/uniceflab para más información sobre el programa y a las Preguntas Frecuentes (FAQs). Puedes enviar su solicitud de participación en el programa desde la siguiente dirección: https://www.f6s.com/uniceflab/apply

Este documento resume las principales características del programa de aceleración, que empezará el 11 de Febrero del 2020, siendo el plazo máximo para recibir solicitudes el 2 de Enero del 2020 a las 15h CET.

SERVICIOS OFRECIDOS BAJO EL PROGRAMA DE ACELERACIÓN

El programa de aceleración desarrollado por ISDI Accelerator para UNICEF Comité Español, es un programa de aceleración de startups de alto rendimiento, que ofrece los siguientes servicios:

Financiación: de un máximo de 50.000€ en total, que se repartirán entre las startups con el siguiente desglose:
- 5.000€ para cada una de las 5 seleccionadas al ser seleccionadas y comprobar su documentación, y al finalizar el “Demo Day”:
- un premio de 10.000€,
- 2 premios de 5.000€ y
- 2 premios de 2.500€.
Formación tipo “bootcamp”: orientado a ayudar a las startups a tener más conocimiento en áreas de negocio, con un formato práctico y ponentes de organizaciones líderes, incluyendo ISDI y UNICEF Comité Español.
Mentores de negocio: sesiones individuales con mentores de alto nivel asignados a cada startup durante tres meses. Siguiendo una metodología personalizada, y trabajando en necesidades reales.
Mentores especialistas: acceso a todos los mentores de ISDI Accelerator.
Programa de “Perks”: son servicios gratis o con un alto descuento incluyendo: servicios tecnológicos, de marketing, legal y talento, entre otros, proveídos por reconocidas empresas como Amazon, Braintree, Marketo, Sendgrid, etc.
Demo Day: al final del programa de aceleración, los finalistas presentarán en frente de UNICEF Comité Español, inversores privados, ejecutivos de grandes corporaciones y/o medios.

Además de estos servicios UNICEF – Comité español valorará la creación y apoyo de pilotos o pruebas de concepto cuyas condiciones se reflejarán, en todo caso, en el correspondiente Convenio de Colaboración que deberá suscribirse entre UNICEF Comité Español y la/s startup/s seleccionada/s.

Contraprestación:

Comercialización preferente. La Start-Up se compromete a colaborar con UNICEF Comité Español de forma preferente en la comercialización/desarrollo de su producto durante un máximo de tres (3) años. Si UNICEF Comité Español rechaza participar en la comercialización de un producto, la Start Up quedará liberada para comercializarlo como desee.
Donación. La Start-Up se compromete a donar a UNICEF Comité Español un 5% sobre la financiación conseguida por la start-up en rondas de financiación tras salir de la aceleradora, limitado a un plazo de 18 meses a contar desde la primera ronda de financiación.
Participación del éxito. En caso de producirse determinadas situaciones de éxito (entre otras, a título enunciativo, proceso de venta o enajenación de la totalidad o parte sustancial de las actividades y/o activos de la Start-Up; operación mercantil con cambio en el control de la Start-Up; o la admisión a cotización de la Start-Up o admisión en un mercado secundario), la Start-Up se comprometería a donar tanto a UNICEF Comité Español como a la Fundación ISDIGITAL un 2,5% a cada una de ellas, del valor de entrada en la Start-Up.
Las condiciones particulares en las que se desarrollará la colaboración entre la Start-Up, ISDI Accelerator y UNICEF Comité Español deberán quedar recogidas por escrito en el correspondiente acuerdo que será suscrito por las partes.

Criterios de Elegibilidad:

El programa de aceleración desarrollado por ISDI Accelerator para UNICEF Comité Español acepta solicitudes de startups. Una startup es considerada como tal si cumple con los criterios de pequeñas y medianas empresas de la comisión Europea, 2003/361/EC. Sólo las startups que cumplan con los siguientes requisitos son elegibles para el programa de aceleración:

Establecidas legalmente
Tienen al menos un CEO o responsable de negocio y un CTO o responsable técnico
Tienen al menos un fundador dedicado a tiempo completo al proyecto
Tienen al menos un MVP (Producto Mínimo Viable) totalmente desarrollado
Están buscando establecer colaboraciones con organizaciones como UNICEF Comité Español en menos de 6 meses
Aceptan los “Criterios y parámetros de debida diligencia de UNICEF Comité Español para colaboración con empresas y start-ups”
El español es el idioma oficial para presentar solicitudes, para optar a ser elegibles y durante todo el programa de aceleración.

ENCAJE ESTRATÉGICO

UNICEF Comité Español, está buscando a las startups más disruptivas de países hispanohablantes, que puedan tener un alto impacto en los desafíos que enfrenta la infancia más vulnerable. La prioridad será para las siguientes verticales, pero todas las ideas innovadoras son bienvenidas:

Nuevas formas de financiación: Desarrollo de nuevas fuentes de financiación y sostenibilidad de las causas sociales, como por ejemplo:
- Micro-donaciones, campañas de crowdfunding, redondeo solidario, ventas solidarias, criptomonedas, donaciones por RRSS, etc.
Nuevo contenido: Fomentar la creación de contenidos y formatos que se adapten a los diferentes públicos objetivos. Ejemplos como:
- Realidad virtual o aumentada, tecnología por voz, contenido móvil, sensores, específico y personalizado para cada público objetivo (desde jóvenes a grandes instituciones)
Confianza, Datos y Seguridad: Mejorar la trazabilidad y gestión de la información para ofrecer confianza, transparencia, obtener información ágil para la toma de decisiones, y ofrecer una experiencia personalizada y segura, como por ejemplo:
- Blockchain, Inteligencia artificial, Big data, Marketing automation, CRM, herramientas predictivas y de perfilado que mejoren el compromiso social, interactividad y efectividad de la comunicación a los distintos colaboradores.
Optimización de procesos clave: Mejorar la eficiencia de los procesos clave las organizaciones no lucrativas
Innovación disruptiva: Desarrollo de nuevos productos, servicios, herramientas, tecnologías y/o modelos de negocios de carácter disruptivo que puedan contribuir a la puesta en marcha, al apoyo y mejora de causas sociales.

ENVÍO DE PROPUESTAS

Las solicitudes deben realizarse a través de la página oficial: https://www.f6s.com/uniceflab/apply. Las solicitudes enviadas por otros medios no serán tenidas en consideración para la evaluación.

El formulario de inscripción se encuentra alojado en la plataforma F6S (https://www.f6s.com/) y, para poder completarlo y enviarlo, es necesario darse de alta en dicha plataforma con los datos personales de, al menos, uno de los miembros del equipo. Al confirmar el envío de datos a través del citado formulario, el usuario está aceptando la política de privacidad de ISDI Accelerator, de Unicef Comité Español y de f6s (https://www.f6s.com/privacy-policy).

Las solicitudes se enviarán en un sólo formulario online, que incluye:

Propuesta de proyecto: dividida en 5 secciones:
- Contacto e información legal
- Proyecto
- Compañía y equipo
- Negocio y encaje estratégico
Preguntas de control: preguntas de opción múltiple relacionadas con dónde y cómo han conocido la aceleradora, políticas de privacidad y “Criterios y parámetros de debida diligencia de UNICEF Comité Español para colaboración con empresas y start ups“

Por favor, antes de enviar la información considera que:

Los Evaluadores sólo considerarán la documentación incluida en el formulario de solicitud (y en los anexos). Es responsabilidad del solicitante incluir toda la información necesaria en el formulario.
Solo se puede presentar una propuesta por solicitante. Si se identifica más de una propuesta por solicitante, solo se evaluará la primera propuesta que se haya enviado en orden de tiempo.
Los datos proporcionados deben ser reales, verdaderos y completos y deben permitir la evaluación de la propuesta.

IDIOMA

El español es el idioma oficial para presentar solicitudes en el programa de aceleración desarrollado por ISDI Accelerator para UNICEF Comité Español. Las propuestas deben estar en español para ser elegibles.

El español también es el único idioma oficial durante todo el programa de aceleración.

DERECHOS DE PROPIEDAD INTELECTUAL E INDUSTRIAL

En la medida en que resulte necesario para la evaluación y, en su caso, la eventual prestación de los Servicios, los solicitantes conceden a ISDI y a UNICEF Comité Español, a título gratuito, con carácter no exclusivo, sin límite geográfico y temporal y con facultad de cesión a terceros, el derecho de uso y explotación sobre los contenidos y materiales facilitados para la recepción de los Servicios.

Los solicitantes serán los únicos responsables de las reclamaciones sobre derechos de propiedad intelectual o industrial que puedan producirse, obligándose a resarcir a ISDI y UNICEF Comité Español , sus empresas participadas y/o los clientes de cualquiera de ellos de cuantos gastos, daños y perjuicios le pueda producir la violación de los referidos derechos.

Queda expresamente prohibido a los solicitantes el uso y/o explotación, de cualquier manera y en cualquier medio (propio y/o de tercero), de los logotipos, marcas y/o signos distintivos tanto de ISDI como de UNICEF Comité Español, a menos que se obtenga la autorización previa y por escrito del titular de los signos distintivos.

Criterios de elegibilidad

Para ser elegible para la evaluación, las solicitudes deberán cumplir:

Deben cumplir con todos los criterios de elegibilidad
Deben enviarse a través de la plataforma oficial https://www.f6s.com/uniceflab/apply
Deben presentarse antes de la fecha límite. Las solicitudes deben presentarse antes de la hora de cierre y la fecha de la ronda del concurso. Las propuestas tardías no serán evaluadas.

CALFICACIÓN PREVIA O “PRE-SCORING”

Las propuestas elegibles serán puntuadas automáticamente por el sistema de evaluación, obteniendo hasta 100 puntos porcentuales de acuerdo con los siguientes criterios: equipo (30% de peso), negocio (30% de peso), encaje estratégico (40% de peso).

El sistema de evaluación establecerá un umbral y todas las solicitudes que tengan, al menos, la puntuación del umbral o mayor, pasarán a la evaluación externa.

EVALUADORES EXTERNOS

Después de la calificación previa o “pre-scoring”, todas las solicitudes serán evaluadas y calificadas por evaluadores expertos independientes siguiendo los siguientes criterios: equipo, negocio y encaje estratégico.

JURY DAY

Se solicitará a un total de 25 finalistas a realizar su presentación oral o “Pitch” en Madrid, donde los 5 ganadores serán seleccionados por un jurado. El jurado estará compuesto por ejecutivos de UNICEF Comité Español e ISDI Accelerator, pudiendo además congregar a evaluadores independientes. La organización estudiará la conveniencia de permitir realizar esta presentación a distancia, usando herramientas de videoconferencia. La organización será la encargada de la evaluación final y selección de ganadores reservándose la facultad de dejar desierta la convocatoria.

Además de estos 5 ganadores, se creará una lista de reserva de las siguientes mejores solicitudes.

PROCESOS DE COMUNICACIÓN

A instancias de UNICEF Comité Español, el programa de aceleración podrá ser cancelado si no concurren las condiciones de calidad esperadas y exigidas a las Start-ups participantes en función de los criterios que a lo largo del programa y en cada una de las fases se irán valorando. UNICEF Comité Español podrá tomar esta decisión en cualquier momento de forma unilateral y la comunicará por escrito a ISDI Accelerator que será la encargada de comunicarlo a las Start-ups.

Esta decisión no generará ninguna indemnización a favor de las Start-ups que no podrán reclamar por ningún concepto ni a ISDI a UNICEF Comité Español.

ROS2 Monitoring Tool

The ROS2 Monitoring component is meant for developers using ROS2: a dashboard for monitoring health, login, examining services, publishers and subscribers associated to ROS2 nodes. The component includes a GUI, which is used to interact with the component, through the GUI the user can setup the ROS components that the user wants to monitor.

Main non-functional requirements

The component has no real-time responsiveness requirements

Software requirements/dependencies

Platform: Ubuntu, MacOS, Windows Requirements: ROS2, Docker

Hardware requirements

64-bit system capable of running: Ubuntu, MacOS, Windows and ROS2

Security threats

The component should operate behind a firewall during production

Privacy threats

No privacy threats have been identified

Execution place

Private Cloud/PC neart production

Deployment instructions

Deployment instructions can be found on a public repository

User interface

A dashboard showing the current status of all ROS2 nodes in the system

Supported devices

Desktop/Laptop, XX

User defined scenarios (non-technical) and relevant pilot cases

The component can be used to monitor a collection of ROS2 nodes

M2O2P: Multi-Modal Online and Offline Programming solution

The main functionality of this component is to enable robot control using natural human actions as input, in this case hand gestures using a gesture tracking glove. With sensor glove by CaptoGlove LLC, the operator makes distinguishable hand gestures to command and control the robot in the process. The component is reconfigurable for different controlling scenarios.

Main non-functional requirements

N/A

Software requirements/dependencies

For the CaptoGlove, there should be Capto Suite installed. Docker installed on the host machine

Hardware requirements

20GB Hard drive space, recommended 2GB RAM

Security threats

None

Privacy threats

None

Execution place

CaptoGlove SDK is ran on host Windows PC and all the other parts of M2O2P are docker images

Deployment instructions

Instructions of application are provided in PDF format, later on video too.

User interface

User interface for the component is mainly in Web UI of the component. This can be reached from host machine navigating to localhost:54400 on browser.

Supported devices

Any Windows 10 machine, CaptoGlove

User defined scenarios (non-technical) and relevant pilot cases

Component can be used in any system where there is a need to send commands or finish tasks by human operator using the glove. In the Siemens pilot case, the component was used to complete tasks in a bin picking collaborative robotic application.

VR-RM-MT: Virtual reality set for robot and machine monitoring and training

The main functionality of this component is to enable the training and support of human workers in collaborative tasks. For doing so, the main activities of the collaborative task and the interaction of worker and robot is created in Virtual Reality (VR). By using a virtual reality headset and equipment the worker can remotely visualize, monitor, and perform the training of collaborative tasks with robots. It should be noted that based on the use case requirements (e.g., workspace and environment, equipment, safety aspects and interfaces to other components), several data inputs might be needed for creation of custom simulations. The component is divided into a sandbox mode (using pre-programmed actions) and a dynamic mode, which depending on configuration could receive data inputs from ROS nodes for on-the-fly creation of tasks

Main non-functional requirements

N/A

Software requirements/dependencies

Windows 10 and compatible browser (Firefox, Chrome, etc)

Hardware requirements

A VR headset supported by A-Frame with controller positional tracking, as listed here: https://aframe.io/docs/1.2.0/introduction/vr-headsets-and-webvr-browsers.html

Security threats

None

Privacy threats

None

Execution place

Private cloud (meaning in pilot premises), cloud

Deployment instructions

Provide information on where deployment instructions for a ready component can be found (e.g. on public or private access repositories or on websites or only upon request, etc.)

User interface

A main configuration page and a sample workcell layout in VR mode.

Supported devices

Desktops, Laptops

User defined scenarios (non-technical) and relevant pilot cases

This component could be used to train workers in a collaborative assembly process by virtualizing the whole procedure in VR and allowing the worker to interact with the robot and components prior to working in the actual setup. It is important to keep in mind that since this application is meant for training, having a concrete step by step process is required to design and fully benefit the collaborative training

DT-CP: Digital Twin – for planning and control

The Digital Twin for control and planning (DT-CP) allows the users to create a virtual replica of the production line facilitated by the use case. The component is divided into two parts: A simulator, to experiment with alternative models to be implemented in the real line, and a monitoring dashboard for having an overview of the line.
The time-based simulator takes as input several configuration parameters (e.g., takt time, shift time), process descriptions and resources (e.g., workers) with different skill set. Hence the user can modify and test different production strategies that would be more complicated and time consuming to test in the real line. The monitoring dashboard can provide the user with an overview on the status of production and provide notification mechanism for alerts when implemented. It should be noted that some functionalities (e.g., data sources for monitoring dashboard, data modelling, and configuration parameters for simulator) are dependent on the use case and might require further adaptations for proper integration in the setup.

Main non-functional requirements

N/A

Software requirements/dependencies

N/A

Hardware requirements

Device capable of handling web-based applications

Security threats

None

Privacy threats

None

Execution place

Private cloud (meaning in pilot premises), cloud

Deployment instructions

Instructions will be provided on the Git page

User interface

The component will be divided into two parts: an online dashboard for monitoring the line in real time and a simulator, to experiment with alternative models to be implemented in the real line.

The monitoring dashboard is intended to follow the flow of product from one workstation to another.

The simulator allows to modify and test different production strategies that would be more complicated and time consuming to test in the real line. The simulator setup consists of four steps: the introduction of the initial process execution parameters, design of the layout, process description assignment and allocation of resources.

Supported devices

Desktop, Laptop

User defined scenarios (non-technical) and relevant pilot cases

Coupled with data collection applications, the monitoring part of the component could be used to have an overview on the process and provide notification and control mechanisms. Data inputs, notification handling, and control mechanisms may be to be further adapted as per use case.

The simulator allows the user to define a time-based simulation of a process assembly by setting concrete configuration parameters (e.g., takt time, shift time) and assigning resources (e.g., workers) with additional resource parameters (e.g., skill set). Th result would be a report on the pre-provided production targets. Specific configuration parameters and settings may have to be further adapted as per use case.

DCF: Data Collection Framework

The Data Collection Framework (DCF) component collects data from shop floor (field devices, sensors, and controllers) and enterprise resource planning (ERP) systems using data adapters for different use cases. DCF can be used at production/assembly lines to have an overview on the collected data from sensors and workstations. The main function of the component is data collection from systems, data storage into databases if needed, and for engineers/supervisor to review and take appropriate action. It should be noted that some of the data adapters (e.g., ERP adapters) may need to be configured and tested on a use case basis.

Main non-functional requirements

N/A

Software requirements/dependencies

Data Transfer and Communication within DCF and Database requires Python installed and some Libraries (opcua, paho.mqtt, pymongo, pandas, json, flask, requests, cx_Oracle, hbdcli)

Hardware requirements

– Windows 7 or 10
– x86 64-bit CPU (Intel / AMD architecture)
– 4 GB RAM
-5 GB free disk space

Security threats

The component requires authentication from server/database before connecting and collecting ERP or shopfloor data and storing the data in database.

Privacy threats

None

Execution place

Both devices connected with local network and on different host address can be connected via MQTT and OPC-UA

Deployment instructions

DCF component will be deployed in docker and relevant instructions will be provided.

User interface

After specifying necessary connection configuration, the DCF module is monitoring the temperature and pressure reading through opc-ua server (left image). If the temperature or pressure is more than the allowed, it is logging the information (e.g. time, value, description) in the database (right image). These parameters can be changed according to the use case.

Supported devices

Desktop, Laptop

User defined scenarios (non-technical) and relevant pilot cases

DCF component can be used at production/assembly lines to collect data from workstation/sensors and apply event processing. For instance, if more time is being consumed to complete the task at specific workstation, this activity can be monitored, and relevant data can be logged in database for engineers/supervisor to view and take appropriate action

ADIN: Adaptive Interfaces

This component creates user interfaces depending on the information collected from the production line devices and the user’s profile. By doing so, relevant task and operation specific user interfaces are composed for the user. Such interfaces could for example display task specific work description (e.g, description of assembly operation) to users and enable the user to confirm completion of task for interaction with external components. It should be noted that if applicable, other interfaces with different functionalities may have to be developed based on the requirements

Main non-functional requirements

N/A

Software requirements/dependencies

N/A

Hardware requirements

Device capable of use web-based application (e.g.: PC or laptop )

Security threats

None

Privacy threats

None

Execution place

Private cloud (meaning in pilot premises)

Deployment instructions

Instructions will be provided on the Git page

User interface

Supported devices

Desktop, Laptop

User defined scenarios (non-technical) and relevant pilot cases

ADIN can be used by workers in an assembly line for assisting them on the task, giving them the specific and relevant information for fulfilling the duty.

It also can be used in collaborative task with cobots where the worker receive instructions on the steps of the collaboration task.

Shakeit: Workcell Process Optimization based on Reinforcement Learning

Human-Centered Process Optimization based on RL in which its main functionality is to provide Reinforcement Learning logic wrapped in ROS2 packages

Main non-functional requirements

Requirements for real-time responsiveness depends on the application. However, since reinforcement learning optimize next action and not the current, real-time responsiveness requirements are relaxed.

Software requirements/dependencies

Platform: Ubuntu, macOS, Windows.

Requirements: ROS2, Docker

Hardware requirements

64-bit system capable of running: Ubuntu, macOS, Windows.

High performance PC/Cloud (good CPU and GPU) for training models.

Eg: +10 core count, +64 GB ram, RTX 2080ti for training (depending on the application and model).

Security threats

When deployed on-premise a firewall should be enough. If deployed in the cloud work is required to ensure a secure connection between the cloud and the production equipment/PC.

Privacy threats

No privacy threats have been identified.

Execution place

Private cloud/PC near robot.

Deployment instructions

Deployment instructions for the component can be found on a private access repository.

User interface

The component will have multiple user interfaces:
A common user interface (dashboard) for developers and end-users containing data visualization, selected actions, and other diagnostics.
Developers will furthermore have a GUI for yaml-file system configuration and all available ROS2 tools for visualization and diagnostics.

Supported devices

Desktop/Cloud

User defined scenarios (non-technical) and relevant pilot cases

The component can be used to optimize a work cell process with reinforcement learning. Example: optimize the process control of a vibration feeder, such that that an element always is available for a robot to pick up.

FBAS-ML: Force-Based Assembly Strategies for Difficult Snap-Fit Parts Using Machine Learning

The component is based on a generic add-on force-control for classical industrial and/or collaborative robots.
An innovative force-sensor based strategy is used to fit two or more parts together that require a snap connection.
The component is a ROS based control approach.

Main non-functional requirements

– Low latency required

– The trained assembly skills can be scaled in time and are primarily limited by the force control performance of the robot (F/T sensor)

Software requirements/dependencies

– ROS framework with ROS control (kinetic or melodic)

– FZI Custom extension of ROS Cartesian Motion, Impedance and Force Controllers

– FZI Custom wrappers for external robotic sensors – Robot ROS driver (e.g. ROS UR)

– TensorFlow 2.1 with python 2.7

Hardware requirements

– Robot with wrist force-torque sensor mounted or integrated

– Dedicated Pc (e.g. i7 shuttle PC with 8 GB ram)

Security threats

The component should run on a separate network without access to the public internet or to any other network not authorized to use it (ROS1 security)

Privacy threats

No specific privacy requirements, no personal information logging

Execution place

Local

Deployment instructions

Internal development, deployment instructions only upon (approved) request

User interface

Text configuration files

Supported devices

Any robot which supports ROS control and can measure end-effector forces and torques (intrinsic or integrated)

User defined scenarios (non-technical) and relevant pilot cases

Force-based assembly tasks which require difficult snap fitting of parts by a robot
Pilot cases: Siemens use case 1

DTS: Dynamic Task Scheduling for Efficient Human Robot Collaboration

Task manager for safe and efficient human-robot interaction

Main non-functional requirements

– Real time responsiveness is fundamental for the task scheduling to work safely and properly. The supervision of the robot pose requires at least 10 checks per second of the environment, for the robot to accurately react if there is any obstacle on its way
– Sensor information (in particular depth information) needs to be as up-to-date as possible

Software requirements/dependencies

– ROS1 Framework
– GPU-Voxels
– FZI Custom extension of ROS Cartesian Motion, Impedance and Force Controllers
– FZI Specific Extension of the FlexBE ROS package or FZI behaviour-Tree Implementation for Task Modelling and Scheduling
– FZI Custom ROS wrappers for external robotic sensors
– FZI Shared workspace (ROS application of GPU-Voxels) for human-robot-collaboration
– FZI Robot Collision Detection ROS package
– FZI Human Pose Prediction and Tracking software (optional)
– Robot ROS Driver

Hardware requirements

– (Depth) Cameras with fast update rate for the images
– Combination of several sensors (one is not enough)
– 1 shuttle PC for robot control with real time optimization (low latency)
– 1 additional PC with GPU for more computational intense tasks (i.e. collision avoidance, human detection)

Security threats

Run on a separate network (ROS1 security) without access to the public internet or to any network not authorized to use it

Privacy threats

No specific privacy requirements. No personal information, camera or 3D data logging

Execution place

Local

Deployment instructions

Internal development, deployment instructions only upon (approved) request

User interface

Text configuration files

Supported devices

Any robot with ROS driver, URDF description and real time joint angles

User defined scenarios (non-technical) and relevant pilot cases

Efficient Human-Robot collaboration on the shop floor, where the robot needs to fulfil tasks in the proximity of the worker
Pilot Use case: Siemens Use Case 1

HA-MRN: Human Aware Mobile Robot Navigation in Large Scale Dynamic Environments

Safety and acceptability of mobile robots

Main non-functional requirements

– Inputs expected at 10 Hz. Outputs between 2 and 10 Hz
– Lower frequencies will influence safety and acceptability severely

Software requirements/dependencies

– ROS1 framework
– Google Cartographer ROS
– Move_Base and/or Move_Base_Flex ROS packages
– AGV ROS driver
– External ROS sensor drivers (cameras, lasers)
– Open Pose
– Wheel Odometry (ROS Topic)

Hardware requirements

– SICK Lidar (for example, SICK)
– Intel RealSense and/or 2D camera
– Dedicated PC (Intel5, High End GPU)

Security threats

Operates inside of mobile robot or secured WIFI connection
(no off premises connection required)

Privacy threats

No personal information logging

Execution place

Local

Deployment instructions

Present: Internal development available on approved request
Future: Public access repositories

User interface

– Text configuration files

– (optional) GUI

Supported devices

– Specific PC (to be embedded in a compatible AGV)

– Any AGV with ROS driver

User defined scenarios (non-technical) and relevant pilot cases

Mobile Robot evolving in an industrial plant or public area with people
Pilot use case: Bosch use cases 1 and 2

FTPT: Flexible Task Programming Tool

Graphical front end (GUI) to program new robotic applications by quickly creating new control sequences based on ROS tools.
The tool helps to develop or change the collaborative robotic applications, gives monitoring feedback on the status of the process and could be used to model different tasks as well as the interaction between robot and human transparently.
It is an alternative to SMACH and FlexBE using Behavior Trees.

Main non-functional requirements

No real time responsiveness required

Software requirements/dependencies

ROS1 Framework

Hardware requirements

A PC

Security threats

Run on a separate network (ROS1 security) without access to the public internet or to any network not authorized to use it

Privacy threats

No specific privacy requirements, no personal information logging

Execution place

Local

Deployment instructions

Internal development, deployment instructions only upon (approved) request

User interface

HTML editor to control the functionalities of the task-programming tool

Supported devices

GUI: Any device that allows mouse-like controls

User defined scenarios (non-technical) and relevant pilot cases

Any scenario which involves programming of robots
Pilot use cases: Siemens use cases 1 and 2, Bosch use case 1

ASA: Automated Safety Approval

This component is used to determine whether the chosen robot trajectory & speed is safe and the required separation distance has been chosen adequately and can be covered by the sensor configuration. (This uses a calculation of the size of the required separation distances for robots that use the operating mode speed and separation monitoring.)

Main non-functional requirements

Trajectories should be checked as early as possible to minimize the delay of execution. Ideally, precomputed trajectories are validated in advance.

Software requirements/dependencies

Currently Visual Components together with the IFF Safety Planning Tool are required for setting up the cell layout. In the future, other tools for this process might be available.
To activate all features and use optimized calculations, a valid license can be purchased from Fraunhofer IFF.
The component runs as a Linux Docker Container on Linux and Windows hosts.

Hardware requirements

PC, no special performance features

Security threats

no known issues

Privacy threats

no known issues (no cameras, no collection or processing of personal data)

Execution place

PC next to robot cell or server. Other options possible (e.g. Private cloud).

Deployment instructions

Deployment instructions will be available on the Shop4CF Docker Registry (docker.ramp.eu)

User interface

No user interface available.
The REST API is documented using Swagger.

Supported devices

PC, Docker

User defined scenarios (non-technical) and relevant pilot cases

When operating a robot cell that uses speed and separation monitoring for safety purposes, you have to check if a given trajecory is safe. If the trajectories are fixed or worst case trajectories can be defined, the operator can check them during design phase (e.g. using the IFF Safety Planning Tool). If trajectories can change (e.g. when using dynamic motion planing), the ASA component allows you to check if a trajectory is safe and the separation distance can be monitored by the sensor configuration. If a trajectory is not safe, the user can calculate a different one or reduce the robot’s speed until all safety conditions are met.
The ASA component ist utilized in the Siemens Use Case 1, where collision-free trajectories are calculated at run-time.

RA: Review of Risk Analysis

The review of the risk analysis supports a safety expert in identifying hazards and estimating risk. The responsible human designer is guided through the formalized process of identifying new hazards based on identified or manually captured system changes (e.g. part changes including geometry and payload; robot changes including speed, reach, tooling; environmental changes including new tables, fencing, etc.). Application highlights where the existing risk estimation requires updates.

Main non-functional requirements

Bidirectional network access from RA server to FIWARE server (if not used as standalone tool)
Port forwarding, firewall configuration
Secret injection via files or environment variables

Software requirements/dependencies

Container runtime, e.g. Docker

Hardware requirements

Server: about 50 MB free RAM / < 1GB disk space.

End-user device: min. 1280×720, ideally 1920×1080 screen, modern web-browser, about 500MB free RAM

Security threats

Production application must use HTTPS reverse proxy. Secrets must be generated and injected securely. Network access should ideally be limited. Additional requirements apply depending on threat model. E.g. when using FIWARE integration and local user management with enabled self-registration, the server must be located inside secure network (on the same permissions level as FIWARE server) due to granted lateral access to FIWARE server (alternatively use authentication via identity server for managing trust).

Privacy threats

Process critical data could be part of the data sent between client and server and this could put partners’ data at risk.

Execution place

Gateway, private cloud (meaning in pilot premises), cloud, etc.

Unrestricted

Deployment instructions

Deployment instructions are a part of the overall documentation and are included in container and available as separate PDF.

User interface

Browser based interface with multiple tabs, available in German and English (with possibility to add additional languages). Documentation (included in container and available as PDF), video see below.

Supported devices

The server host is unrestricted. The end-user device should preferably be laptop or PC (due to size and amount of information on the screen).

User defined scenarios (non-technical) and relevant pilot cases

See ”Main functions”. Additionally, in the FIWARE configuration monitoring mode the RA component will track the resource assignment to the Process on FIWARE server and either automatically communicate previous approval of the configuration or facilitate review by the safety expert.

FLINT

The aim of the FLINT platform is to facilitate the incorporation of current/future wireless IoT devices (sensors/actuators) in a factory or shopfloor setting, as well as the required local wireless IoT communication infrastructure to connect such devices (e.g. LoRa gateways, BLE gateways). This component requires horizontal integration. At the left side, it will make use of adapters to interact with wireless IoT devices and long-range wireless communication equipment. At the right, after performing the required data transformations, it will either represent the IoT device as a LwM2M compliant device that can interface in a standardized way with a LwM2M back-end platform (for instance the open source Leshan platform) or deliver the data in a suitable format to a broker (e.g. FIWARE context broker).

Main non-functional requirements

N/A

Software requirements/dependencies

– Dependency on data formats used by IoT devices / used wireless IoT infrastructure, which requires the 1-time design of suitable input/processing adapters. Similar dependency for output adapters in case no processing to LwM2M.
– Docker: adapters realized as Docker containers. Implementation of adapters can be done in any language.
– MQTT broker: for the information exchange between adapters
– LwM2M processing adapters: dependency on Anjay, a C client implementation of LwM2M

Hardware requirements

Server/cloud platform supporting deployment/management of Docker containers.

Security threats

Currently, the internal communication between the adapters and MQTT broker is not secured. However most deployments are done on a secure company network, so the security risk should be limited.

Privacy threats

Privacy threats will depend on the type of data that is collected by IoT devices)

Execution place

Private Cloud

Deployment instructions

Deployment instructions can be found on https://github.com/imec-idlab/flint. Customization will be needed depending on the IoT devices/infrastructure to be used/deployed.

User interface

Dashboard for monitoring data received from / sent to IoT devices (see screenshot below). However, the user interface is not the core of the component, as it can operate without any UI.

Supported devices

The aim of the platform is to be extensible to support a wide range of wireless IoT devices and technologies.

User defined scenarios (non-technical) and relevant pilot cases

Industrial monitoring, asset tracking, environmental monitoring, etc.

OpenWIFI - Open-source implementation of 802.11 WIFI on FPGA

Supporting human workers on the shop floor by giving them real-time wireless control over aspects such as process management, interactions with robots, collecting sensor data.

Main non-functional requirements

N/A

Software requirements/dependencies

Linux OS, GNU toolchain, Xilinix Toolchain

Hardware requirements

SDR board (e.g. Xilinx ZC706 + FMCOMMS2/3/4 or other compliant board see https://github.com/open-sdr/openwifi )

Security threats

WPA2 encryption is available and should be sufficient. Of course, a network firewall is necessary.

Privacy threats

All data transmitted over the same WiFi network can be seen by all connected clients. So SSL encryption might be necessary.

Execution place

Private cloud (meaning in pilot premises)

Deployment instructions

All information and source code is available on https://github.com/open-sdr/openwifi

User interface

– Developer: interact with openWIFI through linux WiFi driver (e.g. ath9k), and interface to openwifi specific components with a command line program (“sdrctl”)
– User: openWiFi acts as a regular WiFi access point

Supported devices

All 802.11 WiFi enabled devices are supported (smartphones, tablet, laptops, embedded WiFi hardware, WiFi sensors, …)

User defined scenarios (non-technical) and relevant pilot cases

Wi-POS Indoor Localization

The Wi-POS system is able to accurately determine the position of AGVs, robots or equipment on the SHOP floor. Positioning workers is also possible, but might be difficult for privacy reasons.

Its goal is to enhance the safety of humans and support human workers on the shop floor ( Relieving repetitive and hard tasks such as moving equipment).

Main non-functional requirements

N/A

Software requirements/dependencies

Standalone software (full-stack) is deployed on anchor nodes and mobile tags.

Hardware requirements

Dedicated embedded hardware is needed.

Security threats

No encryption on wireless sensor network, so positions could be retrieved. The server that collects the hardware should be protected by a network firewall.

Privacy threats

If the position of humans is logged, then privacy concerns might arise.

Execution place

Private wireless network is setup by the Wi-POS system (on-site)

Deployment instructions

Deployment instructions can be obtained on request. The instructions will vary depending on the location and the use case. System should be plug-and-play.

User interface

Not available. Measured coordinates are only pushed to FIWARE context broker.

Supported devices

Only dedicated hardware (proprietary) is supported for now. Other UWB enabled hardware (e.g. new iPhone) might be supported in the future.

User defined scenarios (non-technical) and relevant pilot cases

Determining the position of AGVs on the SHOP floor to allow navigation through the factory.

Locating important equipment on the SHOP floor.

Defining safe zones around robots to avoid human injuries.

Automated inventory management.

Code will not be publicly available.

PMADAI - Predictive Maintenance and Anomaly Detection in Automotive Industry

Supporting human workers in predicting or preventing potential failures and incidents; supporting human workers in planning services and repairs.

Main non-functional requirements

(non) functional requirements, especially with respect to real-time responsiveness

Software requirements/dependencies

Linux, Windows, MacOS

Hardware requirements

Our app consists of several components (docker images). To use it in a comfortable style, we suggest at least 16RAM, and 60gb of disk space (in order to store OracleDB, InfluxDB, Kafka, Orion). OracleDB spaces is increasing in time in estimate way +/- 0,045MB per one operation (unstable value).

Security threats

Due to Volkswagen security politics, we exchange data between microservices with JWT token. All users, who wants to use our app, should be logged in via LDAP server. In develop mode we can use test user. All address and ports are protected. App is running in internal network without access to external network but it is not required.

Privacy threats

LDAP authentication is required.

Execution place

Everywhere when docker images can be hosted – there is no limitation.

Deployment instructions

Instructions can be found in our bitbucket repository with whole project.

Provided upon request.

User interface

The application has a graphical user interface for viewing current waveforms and checking potential anomalous waveforms. The user can view, sort and filter the results. He can also report his own anomaly event if he deems it necessary. The application’s capabilities are limited only to viewing waveforms and metadata resulting from current waveforms. Data is obtained from the backend using REST API and websockets.

Supported devices

Laptop, PC

User defined scenarios (non-technical) and relevant pilot cases

Currently two potential use-scenarios for this component have been identified:

1. Prediction of failures of a car body lift used in production process. Identification of repair time – which should result in reducing unnecessary interventions by human workers and, at the same time, in preventing future failures.

2. Prediction of repair and maintenance (e.g., cleaning) interventions in parts of the paintshop. Detection of dependencies between observed changes in measurements and quality of paint structure. Again the purpose of this scenario is to reduce unnecessary interventions by human workers and, at the same time, to prevent failures.

Available upon request

VQC - Visual Quality Check

Supporting human workers at production lines by monitoring quality.

Main non-functional requirements

Input requires template image and test sample.

Software requirements/dependencies

OpenCV, Python, Flask. The component can be run with Docker image, then no additional installations are required.

Hardware requirements

A standard PC have enough computing power for this component, as no machine learning strategy was used.

Security threats

The components’ API should be accessible only in local or private network.

Privacy threats

None

Execution place

PC at the operator stand (in pilot), however this can be also run on the remote server.

Deployment instructions

FIWARE must be already up and running. To deploy the component, you can run the docker image directly or go to project folder and run `docker-compose up -d`.

User interface

There is no user interface – other components should call API function.

Supported devices

In Bosch Pilot AR-CVI was used as GUI.

User defined scenarios (non-technical) and relevant pilot cases

Dedicated application for laptop or desktop PC.

Digital Twin for Intralogistics

Supporting human workers on production line.

Main non-functional requirements

AR (augmented reality) application precision of measured distances an location of simulated objects – we expect that AR application accuracy will be between 1cm-5cm per 5m

Software requirements/dependencies

Simulation module based on “LogABS” for Windows

AR application – native app for Android or iOS

Hardware requirements

Intel i7 CPU with 16GB RAM or better for LogABS

Mobile device compatible with AR Core or AR Kit for AR application

Security threats

No specific security requirements.

Privacy threats

No specific privacy requirements.

Execution place

PC and mobile device.

Deployment instructions

Instructions can be found in the code repository with whole project.

Otherwise provided upon request.

User interface

Windows GUI for planning and executing logistic simulation, AR app for planning factory locations and equipment.

Supported devices

Laptop, PC, mobile device compatible with AR Core or AR Kit

User defined scenarios (non-technical) and relevant pilot cases

Planning new, safe AGV routes for redesign factory outline e. g. new product is about to enter into production and factory needs to be redesigned to maintain new storage spaces, new machinery etc.

Internal repo, available upon request

AR Manual Editor

Assistance and training for operators during customised product assembly process, and maintenance operations including recognition of objects, sequence of operations and AR guidance to operators.

Main non-functional requirements

– Mobile devices should be compatible with ARCore/ARKit frameworks.

– Wi-Fi connection is required at the shop floor.

– Minimum brightness levels are required for the AR vision algorithms to work correctly.

– If workers are required to wear gloves, specific mobile device models will be required.

Software requirements/dependencies

WebXR, Django, ARCore, Arkit, Microsoft Mixed Reality toolkit

Hardware requirements

Mobile devices/HoloLens. A server

Security threats

The component need an account management. There is one already implemented but in case to be integrated on RAMP or another platform, further adjustments should be needed.

Privacy threats

None

Execution place

Private cloud provided by TECNALIA with access to pilots. In case there is a need for pilot sites to deploy the component there, it can be done using dockers.

Deployment instructions

In RAMP marketplace and upon request.

User interface

– Interface for the developer: an editor to create AR content in an easy way to guide operators in the shop floor.

– Interface for the Operator (customised by the editor): the AR guidance to be visualised with a mobile devices/HoloLens through different steps and different objects (2D/3D objects, images, video, documents, animations, etc.).

Supported devices

Laptop + mobile devices/HoloLens

User defined scenarios (non-technical) and relevant pilot cases

AR guidance to operator during the assembly of the base plate in collaboration with a robot in the Siemens Use case.

– The shop floor manager/developers add a new manual to the system using the editor, adding all the multimedia assets (3D files, pdf, videos, photos, etc.) and defining, step by step, the entire manual.

– The shop floor manager, once the manual has been added, defines through the editor what type of trigger will activate the augmented reality display. In this case, through the Context Broker we will see in a task for HoloLens is raised to show the different steps of the manual.

– Then he publishes from the editor that manual, so that it can be consumed by HoloLens, defining the type of users and roles that will have permission to do so.

– From that moment any worker of the plant with permissions working on the assembly of a base plate will be able to trigger with his device the visualization in augmented reality of that manual based on the status of the collaboration with the robot supporting him/her.

Not available. As stated in the CA, the tool is not open source so no code will be provided.

AR-based Teleassistance

Supporting human workers on the shop floor: Tele-assistance in maintenance for long distance workers

Main non-functional requirements

An Internet connection (Wi-Fi connection recommended).

Android app: ARCore framework is needed to use the AR functionalities.

Browser client: right now, the best/recommended browser to use it is Firefox.

Depending on the resolution of the real time streaming, the bandwidth has to be in accordance with it, with higher resolution is recommended to use Wi-Fi-connection.

Software requirements/dependencies

Server side: NodeJS, Websockets, Express

Client side: ARCore

Hardware requirements

Server side: UNIX/Linux environment, with enough bandwidth for the number of users to use it

Client side: smartphone device compatible with ARCore; browser can´t be Chrome (Firefox recommended)

Security threats

The component need an account management. There is one already implemented but in case to be integrated on RAMP or another platform, further adjustments should be needed. Server side must use an SSL certificate to send the communication data with https protocol.

Privacy threats

Right now, the authentication part only requires having the client application to log in, so it is necessary not to share them with unknown users.

Execution place

Private cloud provided by TECNALIA with access to pilots. In case there is a need for pilot sites to deploy the component there, it can be done using dockers.

Deployment instructions

In RAMP marketplace and upon request.

User interface

Android app client

Supported devices

Laptop + mobile deviLaptop + mobile devices/HoloLens/HoloLens

User defined scenarios (non-technical) and relevant pilot cases

UC2 in Arcelik for equipment maintenance.

A worker that needs support with any type of physical component or machine, call to an expert colleague in the field. In order to do that, the worker opens the application installed in his smartphone and calls the previously connected expert. The application connects with the expert sharing the back camera of the worker and the frontal camera of the expert. The worker scans the component area or the machine area, and the expert draws through the mobile screen creating indications, as a drawing mode to the worker, giving an augmented reality support. When the support is finished, both exit the application.

Not available. As stated in the CA, the tool is not open source so no code will be provided.

VR Creator

It will allow workers to be trained in the operation of a machine, or manufacturing line for example, through the use of virtual reality. With this web tool it will be possible to create and consume immersive VR experiences (with glasses) oriented to training.

Main non-functional requirements

If video 360 are big files web creator tool will require a PC/Laptop with dedicated (and “good”) graphic card. We will specify more.

Wifi connection is required during training experience creation and consumption.

PC/Laptop and VR HMD should contain a WEBXR compatible browser.

Software requirements/dependencies

WebXR, Django, WebGL

Hardware requirements

PC/laptop and VR HMDs

A Server

Security threats

The component need an account management. There is one already implemented but in case to be integrated on RAMP or another platform, further adjustments should be needed.

Privacy threats

None.

Execution place

Private cloud provided by TECNALIA with access to pilots. In case there is a need for pilot sites to deploy the component there, it can be done using dockers.

Deployment instructions

In RAMP marketplace and upon request.

User interface

N/A

Supported devices

PC/Laptop + VR HMDs

User defined scenarios (non-technical) and relevant pilot cases

Training the operators when using new machines. At this moment we do not have any pilot interested in the component.

Not available. As stated in the CA, the tool is not open source so no code will be provided.

MPMS - Manufacturing Process Management System

MPMS includes the functionality to design processes and describe agents, and execute in automated way the processes by assigning activities to agents. It provides orchestration of activities in a global level, i.e., covering all work cells/production lines of a factory.

Main non-functional requirements

As a logical functional component, MPMS shall be able to:

Automatically execute a sequence of activities

Monitor agents’ availability

Monitor agent’s performance including at least task estimated completion time and task actual completion time

Monitor process current state

Provide right information to agents to perform a task

Handle exceptions on agent, task and process level by halting/resuming their activities and initiating out-of-normal action processes

(re-)allocate appropriate agents to perform a task based on abilities, skills, authorizations, cumulative workload, overall manufacturing system status and availability

Re-allocate agents in response to external events such as safety alerts or sensor failures.

As a software technical component, MPMS shall be able to:

Provide a modeler application to model processes

Provide a process engine to automatically enact process models

Provide tasklist applications to deliver tasks to human operators

Support integration to custom UIs as tasklist applications

Provide integration to local components to deliver tasks to robotic agents

Support various platform environments

Support various DBMS

Be deployed both on premise/cloud

Provide security/authorisation mechanisms

Integrate to middleware/context broker and other components

Support web services

Support REST/JAVA APIs

Support SOA/Interoperability (NF)

Be robust (NF) be runtime scalable (NF)

Be easy to use by both process modelers, developers and end users (e.g., human operators) (NF)

Software requirements/dependencies

MPMS is built on Camunda Platform 7.15.0, Community Edition and runs in every Java-runnable environment. It can support the following environments:

Container/Application Server for runtime components

Apache Tomcat 7.0 / 8.0 / 9.0

JBoss EAP 6.4 / 7.0 / 7.1 / 7.2

Wildfly Application Server 10.1 / 11.0 / 12.0 / 13.0 / 14.0 / 15.0 / 16.0 / 17.0 / 18.0

Databases MySQL 5.6 / 5.7

MariaDB 10.0 / 10.2 / 10.3 Oracle 11g / 12c / 18c / 19c

PostgreSQL 9.4 / 9.6 / 10.4 / 10.7 / 11.1 / 11.2 postgres:14-alpine (Docker image)

Microsoft SQL Server 2012/2014/2016/2017

H2 1.4

Adminer:4.8.1 (UI for DB management) (Docker image)

Web Browser

Google Chrome latest

Mozilla Firefox latest

Internet Explorer 11

Microsoft Edge

Java

Java 8 / 9 / 10 / 11 / 12 / 13 (if supported by your application server/container)

Java Runtime

Oracle JDK 8 / 9 / 10 / 11 / 12 / 13 IBM JDK 8 (with J9 JVM) OpenJDK 8 / 9 / 10 / 11 / 12 / 13

Openjdk:11.0.13-jre-slim (Docker image)

Camunda Modeler

Windows 7 / 10

Mac OS X 10.11

Ubuntu LTS (latest)

The Camunda Community Platform is provided under various open source licenses (mainly Apache License 2.0 and MIT). Third-party libraries or application servers included are distributed under their respective licenses. Detailed info on licences is provided in T7.2.

Hardware requirements

For deploying MPMS in a local desktop PC, not any special requirements are needed. A powerful processor, a lot of RAM memory and a decent graphics card is sufficient. The following specs should do:

Processor: Intel Core i7-7700 @ 3.60GHz / Intel Core i7-6700K @ 4.00GHz / Intel Core i7-7700K @ 4.20GHz / Intel Core i7-8700K @ 3.70GHz

Storage: SATA 2.5 SSD (e.g. 256 GB) RAM: 32GB DDR4-2133 DIMM (2x16GB) (well, even 16GB will not be a problem)

Graphics Card: Any modern standard graphics card

Monitor, keyboard and mouse are essential. Touchscreen for operators might be handy.

A laptop could also work:

Processor: Intel Core i7-7700HQ @ 2.80GHz / Intel Core i7-6770HQ @ 2.60GHz.

Security threats

MPMS connects to DBs with password access.

Users of web applications have access with password.

Privacy threats

Agents (and specifically human operators) should be described with due diligence wrt privacy data.

Execution place

MPMS shall be deployed on local PCs on premises. It can also be deployed on a cloud server (e.g., on TUE premises) but extra security is required.

Deployment instructions

Deployment instructions and user manuals can be provided upon request.

Can also be uploaded on RAMP if there’s a specific repository.

User interface

Three different types of users:

Process modelers

They use the Modeler application to model manufacturing processes (with BPMN 2.0)

Application developers

They turn the process models designed by the process modelers into executable process models (i.e., the ones that the Process Engine can interpret and enact).

Also, they can build custom applications (e.g. tasklists, cockpits, smartwatch apps)

(Human) Managers and operators (end-users)

Managers use the Cockpit/Dashboard and Admin applications (default or custom) to see the processes state and manage the users of MPMS

Operators use the Tasklist applications (default or custom) to receive tasks and provide input (e.g. task completion confirmation)

For each type of user, there are manuals and webinars to provide instructions.

Supported devices

Modeler runs on PC/Laptop (see SW/HW requirements above)

Process Engine runs on PC/Laptop (see SW/HW requirements above)

Web applications run on PC/Laptop/tablet/smartphones (additionally, a prototype tasklist application has been built for smartwatch)

User defined scenarios (non-technical) and relevant pilot cases

MPMS can be used in any pilot/open call for:

process modelling (for bottlenecks identification and enabling automated execution),

dynamic agent allocation,

process orchestration,

automated process execution,

integration to other IS (e.g., ERP) for getting the right information and providing it to agents during execution,

process status monitoring,

task monitoring for job safety and quality of human operators,

re-allocation of agents when job safety and quality criteria are violated,

etc.

AR-CVI - AR for Collaborative Visual Inspection/AR for Task Instructions

The component provides visual support in manual assembly and inspection tasks. Human worker is guided by the visualized instructions while performing the associated tasks. The component can project the instructions on a surface or a screen. This provides a clean and structured working environment.

Main non-functional requirements

The Fiware messages are checked at 2 Hz.

Software requirements/dependencies

Ubuntu:

Ubuntu 18.04 or later (for running on Ubuntu)

Docker version 20.10.6 (Previous versions later than v19 are also supported)

Windows:

Windows 10

WSL 2

Docker version 20.10.6 (Previous versions later than v19 are also supported)

Windows X Server (for running on Windows)

Hardware requirements

A screen with at least 1920×1080 (HD) resolution support

A projector [Optional] with at least 1920×1080 (HD) resolution. Front surface mirrors might be necessary for projecting down to a table. High brightness according to the illumination of the environment (~4400 Lumens)

A PC or laptop (Preferably a 4-core CPU with 8 GB RAM, 15 GB HDD space)

Security threats

No specific security threats

Privacy threats

No specific privacy threats.

Execution place

The component should be executed in the assembly cell pc (local deployment). Necessary files can be mounted to the Docker container from the host machine or from a remote machine.

Deployment instructions

Currently in the public Github repository: https://github.com/emecercelik/ar-cvi/ Later the instructions will be available in RAMP.

User interface

The instructions are displayed in a full-screen mode.
User can provide inputs to the component using the buttons on the screen if defined with the display messages (templates).
A screen view is provided below:
Instruction images, PCB quality check outputs (provided by another component), a written instruction, and buttons (at the bottom).
For a developer demonstration refer to the following video:
https://syncandshare.lrz.de/getlink/fiC2jAGB9vBsmaRqtVVmzEg4/ar-cvi_demonstration.mp4

Supported devices

PC, Laptop, Projector, Screen

User defined scenarios (non-technical) and relevant pilot cases

Support on the assembly cell for the human worker by displaying manuals of an assembly or inspection task. The operator can provide inputs to start the inspection (provided by another component).

WoT-IL - Interoperability Layer through Web of Things

It supports the process management improving the interoperability of the system. It addresses the ability of the system to be standard interoperable.

Main non-functional requirements

In the case of OPC UA communication, the server must be configured and deployed. The Node ID (s) must be provided to be able to read the information in real time.

Software requirements/dependencies

Node JS and/or Java but it can be developer also with other programming languages.

Docker and Docker compose

Orion LD Context Broker

Hardware requirements

16 Gb RAM, 10 Gb HD

Linux – Ubuntu

Security threats

It wraps REST APIs in another descriptor, the component does not manage the security of the APIs described. The server that serves the descriptor will be secured through HTTPS and certificate.

Privacy threats

No specific privacy threats.

Execution place

The component should be executed in a local PC on the shopfloor

Deployment instructions

We provide a docker container with a configuration file.

User interface

In this version a Command Line Interface has been implemented. It is expected to develop a graphical interface for the next version.

Supported devices

PC/Laptop

User defined scenarios (non-technical) and relevant pilot cases

A pilot or a SHOP4CF component owner wants to provide to the external word some functionalities he wants to test with external users (for instance for open calls), he builds a WoT (Web of Things) interface with our component that wraps its component to be used by third party developers.

Guía de solicitantes

2 de Enero del 2020 a las 15h CET

ROS2 Monitoring Tool​

M2O2P: Multi-Modal Online and Offline Programming solution

VR-RM-MT: Virtual reality set for robot and machine monitoring and training

DT-CP: Digital Twin – for planning and control

DCF: Data Collection Framework

ADIN: Adaptive Interfaces

Shakeit: Workcell Process Optimization based on Reinforcement Learning

FBAS-ML: Force-Based Assembly Strategies for Difficult Snap-Fit Parts Using Machine Learning

DTS: Dynamic Task Scheduling for Efficient Human Robot Collaboration

HA-MRN: Human Aware Mobile Robot Navigation in Large Scale Dynamic Environments

FTPT: Flexible Task Programming Tool

ASA: Automated Safety Approval

RA: Review of Risk Analysis

FLINT

OpenWIFI - Open-source implementation of 802.11 WIFI on FPGA

Wi-POS Indoor Localization

PMADAI - Predictive Maintenance and Anomaly Detection in Automotive Industry

VQC - Visual Quality Check

Digital Twin for Intralogistics

AR Manual Editor

AR-based Teleassistance

VR Creator

MPMS - Manufacturing Process Management System

AR-CVI - AR for Collaborative Visual Inspection/AR for Task Instructions

WoT-IL - Interoperability Layer through Web of Things

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