Date & Location: 24.05.2024 17:00-21:00

We expect many visitors and will prepare some beverages. Please let us know if you plan to join! 

Chair of Cyber-Physical-Systems 
Metallurgiegebäude 1.Stock
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria

https://youtu.be/jLAUo_Kk01E

Impressions of the last open lab day in 2024.  

English: Immerse yourself in the fascinating world of artificial intelligence and robotics. We present self-learning robots, mobile robot guides and how deep neural networks are learned. Children can experiment with our Lego EV3 robots and try to deliver snacks autonomously. Catering will be provided.

Deutsch: Tauchen Sie ein in die faszinierende Welt der künstlichen Intelligenz und Robotik. Wir präsentieren selbstlernende Roboter, mobile Roboterguides und wie tiefe neuronale Netze gelernt werden. Kinder können mit unseren Lego EV3 Robotern experimentieren und versuchen Snacks autonom auszuliefern. Für Verpflegung ist gesorgt.

The pictures above are from October 2023 and will be updated after the event. 

Program

You have no prior experience with deep learning or robots but would like to work with them?

If so, this hands-on project will enable you to build and control your state-of-the-art robotic devices, such as compliant robot arms, five-fingered robot hands, mobile robots, legged robots, or tactile and visual sensors.

You will use Python for programming. Prior experience is beneficial but not mandatory. 

At the end of the practical project, we discuss your achievements and what you have learnt.

You can work on your own or build a team of up to three people at most. We provide a student lab with high-performance pcs with RTX 4090 graphics cards and student rooms.

The project is based on code examples, wiki pages and video tutorials for non-experts.

Links and Resources

• MUOnline 
• Have a look at our robots and hardware
• Programming in Python Slides by Univ.-Prof. Dr. Elmar Rueckert

Location & Time

• Location: Laboratories at the Chair of Cyber-Physical-Systems.
• Dates:
  • Kick-off 11.03.2024 12:00 -14:00 CR IL/IT.
  • Regular (typically weekly meetings) at the chair or online. 
  • Projects using real hardware will be conducted at the chair.

Learning objectives / qualifications

• Students get a practical experience in working, programming and understanding autonomous robots in navigation and obstacle avoidance tasks.
• Students understand and can apply classical robot path planning and navigation algorithms.
• Students learn how to present their implementation, assumptions and achievements.

Robot serial number:20225300304

Passwords:

• safety: 0000

Supervisor: Vedant Dave, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 15th August 2023

Theoretical difficulty: Mid
Practical difficulty: High

Abstract

The aim of this Thesis is to predict the electricity price for the Hydrogen plants from open-sourced Energy data provided by the European Network of Transmission System Operators (ENTSO-E) [1]. We explore multiple machine learning techniques to achieve this aim. At the end, a standalone GUI is provided, that can be used in the industry with ease. This work was done in collaboration HyCenta Research GmbH.

Further, this thesis seeks to address the following research questions:

• How do different determinants such as the electricity mix (the proportion of energy from various generation sources), in-house generation, and gas prices, influence the cost of electricity?
• Which machine learning approaches/algorithms are most suitable for accurately predicting future electricity price trends, particularly in Austria or other European countries? 
• To what extent does the sensitivity of our model to inputs, like solar and wind energy, affect its overall accuracy and reliability in predicting electricity prices?

Thesis

Deep Neural Energy Price Forecasting for the Hydrogen Industry

Tentative Work Plan

To achieve the objectives, the following concrete tasks will be focused on:

• Literature review
• Evaluation of SOTA methods
• Designing network and hyperparameter tuning
• Evaluation on unseen country’s data
• Development of Standalone GUI

Related Work

[1]  Hirth, Lion & Mühlenpfordt, Jonathan & Bulkeley, Marisa, 2018. “The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform,” Applied Energy, Elsevier, vol. 225(C), pages 1054-1067.

Supervisor: Fotios Lygerakis and Prof. Elmar Rueckert

Start Date: 1st March 2023

Theoretical difficulty: low
Practical difficulty: mid

Abstract

As the interaction with robots becomes an integral part of our daily lives, there is an escalating need for more human-like communication methods with these machines. This surge in robotic integration demands innovative approaches to ensure seamless and intuitive communication. Incorporating sign language, a powerful and unique form of communication predominantly used by the deaf and hard-of-hearing community, can be a pivotal step in this direction. 

By doing so, we not only provide an inclusive and accessible mode of interaction but also establish a non-verbal and non-intrusive way for everyone to engage with robots. This evolution in human-robot interaction will undoubtedly pave the way for more holistic and natural engagements in the future.

Thesis

ROS2-based Human-Robot Interaction Framework with Sign Language

Project Description

The implementation of sign language in human-robot interaction will not only improve the user experience but will also advance the field of robotics and artificial intelligence.

This project will encompass 4 crucial elements.

1. Human Gesture Recognition with CNNs and/or Transformers – Recognizing human gestures in sign language through the development of deep learning methods utilizing a camera.
   • Letter-level
   • Word/Gloss-level
2. Chat Agent with Large Language Models (LLMs) – Developing a gloss chat agent.
3. Finger Spelling/Gloss gesture with Robot Hand/Arm-Hand –
   
   • Human Gesture Imitation
   • Behavior Cloning
   • Offline Reinforcement Learning
4. Software Engineering – Create a seamless human-robot interaction framework using sign language.
   • Develop a ROS-2 framework
   • Develop a robot digital twin on simulation
5. Human-Robot Interaction Evaluation – Evaluate and adopt the more human-like methods for more human-like interaction with a robotic signer.

Quelle: Obersteirische Rundschau (www.rundschau-medien.at)

Dear CPS team, thank you very much for this great and successful year. We all enjoyed our Christmas party at the kitchen. 

New Checklists from SSC

As of 20th of January 2024, the Vice Rector for studies, digitalization and international affairs, Thomas Prohaska, sent out new general guidelines for Mastertheses on the Montanuniversity of Leoben.

English Versions

• Guideline Supervision of Mastertheses
• Checklist Supervision of Masthertheses

German Versions

• Leitfaden Betreuung von Masterarbeiten für Betreuende
• Checkliste Betreuung von Masterarbeiten für Betreuende

German Version of the Checklists – CPS

• Checkliste Betreuer
• Checkliste Student

English Version of the Checklists – CPS

• Checklist Supervisor
• Checklist Student

FFG Projekt 01/03/2024-28/02/2027

Aushubmaterialien machen mit rund 42 Mio. t/a fast 60 % des österreichischen Abfallaufkommens aus, von denen 73 % deponiert und nur 8 % in Behandlungsanlagen eingebracht (BMK, 2021) und deren Outputströme größtenteils einer geringwertigen Verwendung, z.B. Untergrundverfüllung, zugeführt werden. Gleichzeitig werden in Österreich 55 Mio. t/a grundeigene mineralischer Rohstoffe abgebaut (Statista, 2022). Ursache für diese Diskrepanz sind die Herausforderungen bei der Materialdisposition, aber auch die günstige (ALSAG-freie) Deponierung von nicht kontaminierten Aushubmaterialien. Somit stellt die Verwendung von Aushubmaterialien einen ungenutzten Beitrag zur Kreislaufwirtschaft dar, welcher sich vor allem in der Schonung heimischer Ressourcen und in der Minimierung des CO₂ Ausstoß von Tiefbauprojekten bemerkbar macht (Galler, 2015).

Projektziele

• Erörterung von nachhaltigen Verwertungswegen aufgrund der geotechnischen, mineralogischen, petrographischen, geochemischen und hydrogeologischen Ergebnisse aus der geologischen Vorerkundung von geplanten und im Projekt bearbeiteten Tief- und Tunnelbaustellen
• Sensorbasierte Stoffstromcharakterisierung mittels online-Analyse von Wert- bzw Störstoffen durch LIBS und HyperSpecs sowie mineralogische Zusammensetzung durch Raman Spektroskopie am Förderband unter realen Bedingungen in der Tunnelforschungsanlage „Zentrum am Berg“ der MUL
• Entwicklung eines Qualitätssicherungssystems durch Erstellung eines Klassifikationsmodells, welches das Material durch KI-gestützten Abgleich der Ergebnisse der online Analyse mit gesetzlichen Anforderungen verschiedenen Recyclingpfaden bzw. Deponieklassen zuführt
• Baustofftechnische Überprüfung der aus dem Aushubmaterial hergestellten Produkte

Projekt Consortium

• Montanuniversität Leoben
  • Lehrstuhl für Subsurface Engineering (Koordinator)
  • Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft
  • Lehrstuhl für Cyber-Physical-Systems
• AGIR Austria GmbH
• AiDEXA GmbH
• LSA Laser Analytical Systems & Automations GmbH
• Austin Powder GmbH
• Master Builder Solutions
• Edaphos Engineering
• Daxner & Merl
• Universität Innsbruck, Materialtechnologie Innsbruck (MTI)
• ÖBB-Infrastruktur AG

Fördergeber

• Österreichische Forschungsförderungsgesellschaft mbH (FFG)

Project Information

In the NNATT project model research and experimental work is conducted to identify tunnel- and excavated material with sensor based classification and deep learning. Representative tunnel- and excavation material from Austria is sampled, mineralogically, chemically and geotechnically characterized, sensor based measured in preliminary tests and finally applied in a conceptual pilot plant for material classification at the Zentrum am Berg in Eisenerz. Additionally, the project focuses on opportunities for application in alternative building materials, resulting in saving of primary raw materials and the associated reduction of CO2 emissions.

In 2021, excavated materials, such as tunnel excavation, accounted for around 46.1 million tons, or 60% of Austria’s total waste. Our project proposes an innovative solution for the recycling of excavated materials from tunneling and construction projects by using spectral imaging technology data in deep neural networks to predict rocks and their recycling potentials. By implementing real-time material identification on a conveyor belt based on deep neural networks, we aim to feed the excavated material into a recycling chain. This cutting-edge technology enables us to identify the resources potential of the material, facilitating efficient processing and sorting both on-site and off-site.

The objectives of our project are fourfold: to conserve valuable resources in Austria by maximizing material reuse, to reduce the burden on landfills by minimizing waste disposal, to shorten transportation routes and decrease CO2 emissions associated with material transport, and to promote sustainable practices within the construction industry.

Through the integration of advanced technology and a commitment to sustainability, our project represents a significant step towards creating a future in which excavated material is considered a valuable resource that contributes to a circular economy.

Poster

• Abfallwirtschaftstagung: Click here for poster
• Exzellenzcluster – Recycling: Click here for poster

Other

Website AVAW: https://www.avaw-unileoben.at/de/forschung/projekte/nnatt