OMiLAB-Rob Experiments

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Use Case: Interact with different real world objects
Approximation Problem: Items have a complex shape in 3 dimensions

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Use Case: Assemble a product by incorporating components
Planning Problem: Reaching the goal state in a complex environment

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Use Case: Capture characteristics of a system without irrelevant details
Modeling Problem: Finding the right degree of domain-specificity

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Use Case: Enable new experiences for users with context aware devices
Sensor Fusion Problem: Integrate sensor information in a consistent model
We have developed a dynamic pricing system that generates a dynamic price for the end customer based on sensor data and general environmental variables. A joystick for manual control is also integrated.
The aim of this project is implementation of an intelligent priority queue system, that will optimize a resource use and contribute to the environmental protection.

Live

This project aims to address the problem of personalisation in routing. Furthermore it also includes a dynamic pricing approach.
The aim of this project is the integration of physical and model environment within a SmartCity Scenario. ADOxx http requests are used to establish a connection between smart city model and MBots.
Cars are equipped with the option to monitor their own state in terms of an internal covid-19 viral load. This state is communicated to potential customers. Also, environmental factors are analysed to guarantee a sufficient amount of safe cars.
We've implemented a CPS that can be used either in a manual mode, herby navigating it by means of a joystick, or it can be used in an automated fashion, as it follows a predefined track and enters an emergency mode if it deviates from this track.
The aim of this project is a smart tourguide who goes through a physical world with attractions, which you modelled before and gives you information about it by image recognition.
The aim of this project is to implement the use case of dynamic pricing with the use of semantic technologies like image recognition and rule-based systems. In addition the mBot is enhanced with an rgb-led module that is integrated in the use case to
The CPS is driving around virtual Vienna, picking up and dropping off customers at different Point of Interests (POI). The CPS intelligently decides, to which POI it should relocate itself to, to increase the chances of picking up customers.
The project aims at exploring the possibilities of smart leisure appointments utilizing smart vehicles and semantic algorithms.

Live

The aim of this projects is the implementation of an intelligent offering with the use of semantic technologies.
The goal of the project is to extend the current state of on-demand mobility by using the extracted information from the environment to represent it in a way that provides knowledge to the CPS.
The Design 2 Model Approach facilitates the Digital Transformation of the artifacts of a Design Thinking Workshop like SWOT analysis into an equivalent ADOxx Model.
This project combines a user-generated model of a greenhouse with multiple sensor inputs and a rule-based-system to enable an ideal environment for various crops.
Use Case: Selbstidentifikation eines CPS sowie dessen Darstellung und experimentelle Möglichkeiten mittels ADOxx
This service reliefs airport customers from the burden of the baggage. The baggage is picked up at the customer's house and delivered directly to the airport. The weight is already determined on the way.
The Smart Drone Tourist Guide is an autonomous drone which accompanies a user on a sightseeing tour.
The aim of this project is to provide an universal automated link between human "design" thinking and CPS run-time environment.
Use Case: Simulation of a delivery process in warehouses
Ontology: Semantic Annotations using the SeMFIS modelling toolkit
Use Case: Learn and play a simple piano song.
Genetic Algorithm: Learning the keys in the correct order
Use Case: Construct a domain-specific modeling method
Metamodeling: Service-based ADOxx extension with scripts
Use Case: Mixing a cocktail by receiving speech input
Integration: Combine different technologies, models and methods
Use Case: Towers of hanoi with three discs
Constraint Satisfaction: Formalize the puzzle as a Constraint Satisfaction Problem
Use Case: Conceptual model of communication
Metamodeling: New Library in Adoxx including GraphReps
Use Case: Autonomous movement of robots
Fuzzy Logic: Handle fuzzy sensor data input
Use Case: Autonomous parking of vehicles
Service-driven Enrichment: Model-based service extension
Use Case: A smart assistant for model model interaction and cyber-physical systems
Communication Problem: Support cps2human interaction through NAO
Use Case: Paint graphrep as scalable SVG image
Rule Engine: Parse XML markup and control actuators
JavaScript-Based Playground for Tag Detection and AR-Technologies
Use Case: Control robots with gestures
Computer Vision: Learn and interpret gestures to carry out specific actions
Use Case: CPS movement within an environment which uses a tag based object location identification
Image Recognition: Video based coordinates and object assessment
Use Case: Coordinating CPSs in a delivery on demand production line
Communication Problem: cps2cps and cps2human communication
Automization of the order preparation process in a warehouse using robotic workers.
Use Case: A robot interprets the mood of a conversation.
Sentiment analysis: Interprets emotions
Providing a modelling method to manage and organize a self-driving vehicle for package delivery, as well as planning the most efficient route.
Use Case: Analyse a table soccer game Image Recognition: Detect a specific object
The project aims to demonstrate a smart garden enviorment where different agents interact with different object. The data of different RFID sensors are used as input for a rule engine, which executes the appropriate rule.
Use Case: Collision prevention for MBots in a factory of the future.
Sensor based detection of obstacles and MBOTs
This project is based on the sIOT framework and smart models. The aim is to model CPS environments that can be easily understood by humans and processable by machines. Other models must be aware of the CPS environment and take decisions accordingly.
Use Case: With "Energy Blockchain Control" the User is able to model his Smart Home and set preferences for Energy Controlling and Trading within a peer to peer Network.
Errors in the execution of the model can be hard to detect. This project uses ontologies to store the informations about possible errors.
Use Case: Route planning and picking job execution in warehouse environments.
Genetic Algorithm: Metamodel based optimal picking job planning, execution, and simulation.
Modify Scene2Model models via speech input and voice control.
The aim of this project is to automatically provide a list of cars for a specific type of character (business man, worker etc.). The cars are selected according to the preferences of the selected character.
TBD
Automatically create Capabilities from Components, as well as Components from Capabilities for s*Iot methodology.
Use Case: Smart Post - Sending Parcels
The project models a real life cross-road and simulates a new Smart Light Infrastructure by calculating the priorities of each street.The project aims to reduce the time spent in the cross-road as an everyday problem.

Driven by the development plan of the Univerity of Vienna, and inspired by the Open Models (OMi) Laboratory, the OMiLAB-Rob project provides a dedicated research and experimentation space to understand, model, and engineer the knowledge-intensive systems of the future. Both a physical and virtual place, OMiLab-Rob links together

  • a research methodology,
  • a dedicated community and
  • a technical environment.
The objective of the OMiLAB-Rob project is to integrate conceptual models (which are constructed by humans for humans) with cyber-physical systems. Thereby, the focus is to enable a knowledge-based understanding of "smart" models by machines. To facilitate the engineering of these systems, the OMiLAB-Rob project provices a physical and virtual environment, where experiments validate the integration of conceptual models and cyber-physical systems. In a continuous cycle, the experiments' results are used to refine the necessary methods.

Three Layer ArchitectureOMiRob Laboratory