Laufende Projekte, Bachelor- und Masterarbeiten

• Retrofitting of a Cyber-Physical System to a reactive molding machine for thermoset resins

Meeting 15/06

Research

• reviews for ECAI (2/6) (Vedant is working on one of them)
• Research leads:
  1. Dimensionality Collapse of Visual Representations in Reinforcement Learning
  2. Improve SwAV architecture by creating better latent space clusters with the use of Sparse Autoencoders

PhD Registration

• waiting for admission office response

M.Sc. Students/Interns

• Iye Szin has a working prototype

ML Course

• Tutorial on pytorch
• pending grading for assignments 5 and 6

Miscellaneous

• Summer School
  • Registration done
  • Air tickets booked
  • accommodation booked
• English course got postponed

Meeting 22/06

Research

• Reviews for ECAI 2023 done.

M.Sc. Students/Interns

ML Course

Miscellaneous

Meeting 29/06

Research

M.Sc. Students/Interns

ML Course

Miscellaneous

Meeting on the 19th of July 2023

Agenda

Location: Scholz Rohstoffhandel, Industriestraße 11, 2361 Laxenburg

Date & Time: 19th June 2023, 11am to 12pm

Participants: Melanie Neubauer, M.Sc.

Top 1: Notes

• Scholz processes about 30 thousand tons of scrap, about 1 million tons in Austria per year. Sources of scrap: consumers, industry, collection yards of municipalities, repair shops.
• Aluminium, Messing, Kupfer, Kunststoffe (Gummi), Draht – are the problematic components. Bateries and accumulators are also in the scrap and need to be sorted out. Copper is the most important part that must be removed, as this greatly affects the quality of the steel.

• Scrap consists of end-of-life vehicles and whole or crushed and from old household appliances. The cars are disassembled beforehand (tires, fluids, engine, etc.)
• This scrap is used to produce the so-called E40 material, for which there are EU guidelines, but the requirements of the steel producers are higher. The demands of steel and plastics manufacturers must be met. (single-variety) (EU – 2.5cm, Scholz – 10-15cm)
• Criteria in the steel plant: radioactivity, weight, visibility (copper!, plastics, rubber, substances)
• For the E40 material, 1 car and then 2 portions of household scrap are always put into the shredder. The 1:2 blends look about the same as just car scrap. Mixing is done to prevent canting in the shredder. By means of an eddy current separator (use air), the shredder output is divided into the light fraction and the E40 metal fraction. With the help of manual re-sorting, the quality of both outputs is improved. The re-sorting takes place on the two conveyor belts (light fraction, E40 fraction).
• The cameras for data acquisition should be implemented in the area at the hand sorting. If this does not work, a bypass can be created. Also tests can be carried out in the technical center with approx. 5000 stk scrap (Alexia says this is a suitable quantity). 
• technical center in St. Michael owns: NIR, WIS, laser triangulation 
• From next spring, cars will be more partially dismantled before shredding, there will be a re-data collection.

• Libs sensor: very complex and lengthy, has already been carried out at Scholz, silicon and magnesium are detected by it, laser shoots particles out of scrap particles and analyzes the composition
• MBT material: That waste which is removed from the household residual waste with the help of a magnet. This waste goes directly into the shredder, then metal is sorted out again, the rest goes back to the incinerator. (currently not relevant)

Meeting on the 20th of July 2023

Agenda

Location: Chair of CPS

Date & Time: 20th July 2023, 8am to 9am

Participants: Univ.-Prof. Dr. Elmar Rueckert, Melanie Neubauer, M.Sc.

1. Organisatorial progress update by Melanie.
2. Next steps of Melanie.

Top 1: Organisational Update

Update to the Visit to Scholz from 19th of July. (Images are in the Cloud under Projects/Kiramet)

Top 2: Next steps

• Paper for RAAD conference to be submitted. Deadline for submission is 12/20/2023. 
• This should focus on one/ or more methods for segmentation of problematic particles in scrap. 
• Topic: application of classification and segmentation to scrap particles. 
• Methods used: Mask R-CNN, …
• The classes that occur are to be described. 
• In the chapter Analyses will be analyzed how much of what is present in the sample in percent.
• First of all the paper should be written down in note form.
• Chapter: Introduction, Methods, Analyses, Summary….

Master Thesis Student at the Montanuniversität Leoben

Short bio: Klemens is an Energy Engineering student at Montanuniversität Leoben,  working on a Master’s Thesis named “Deep Neural Energy Forecasting for  
Economic Resource Usage in Hydrogen Industries”. This work focuses on  exploring how AI can be used to better manage resources in the hydrogen industry.

Klemens got his start in Electrical Engineering, graduating from a  technical secondary school. After a brief but interesting stint with the Military Orchestra in Carinthia, he decided to return to his  engineering roots, earning a Bachelor of Science in Raw Materials Engineering.

Now, as a Master’s candidate, Klemens hopes to combine his skills and  interests to make a positive contribution to the energy sector.

Research Interests

• Deep Learning
• Resource utilization in Energy Sector

Thesis

•  
• Supervision: Vedant Dave, M.Sc.

Contact

Klemens Lechner, B. Sc
Master Thesis Student at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria 

Email: klemens.lechner@stud.unileoben.ac.at

Meeting on the 5th of June 2023

Agenda

Location: Chair of CPS

Date & Time: 5th June 2023, 11am to 12pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Melanie Neubauer, M.Sc.

1. Organisatorial progress update by Melanie.
2. Infos from Elmar regarding the progress.
3. Next steps of Melanie.

Top 1: Update Melanie

•  Moodle access works
•  Visit St. Michael with my own car on 13.06.
• Home Office on 14.06.
• Problems with the SparseDetr Code on my Laptop (running the code is not possible), running the code in the lab on the 3090 is possible

Top 2: Infos from Elmar

• In St michael extra garbage is delivered to record data
• Alexia has examples of images of the garbage
• Not much open source data for the project in St. Michael available
• Possibility to run the DMS paper Code, and use it for Linus’ Roboter

Top 3: Next Steps

•  Check references from the project paper (E-Mail Elmar)
• Train SparseDetr on the 3090 GPU (check how it works)
• DMS paper Apple (https://machinelearning.apple.com/research/dense-material)
  try to get access to the data and run the code
• Read Paper “Applications of convolutional neural networks for intelligent waste identification on recicling: A review” (interesting information about datasets,…) 

Meeting on the 13th of June 2023

Agenda

Location: Chair of CPS

Date & Time: 13th June 2023, 11am to 12pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Melanie Neubauer, M.Sc.

1. Organisatorial progress update by Melanie.
2. Feedback to the research talk presentation by Elmar.
3. Topics of promising future research direction.
4. Next steps of Melanie.
5. Date of the next meeting.

Top 1: Organisational Update

add you text here. 

Meeting on the 23th of May 2023

Location: Chair of CPS

Date & Time: 23th May 2023, 10:30 am to 11:00 am

Participants: Univ.-Prof. Dr. Elmar Rueckert, Melanie Neubauer, M.Sc.

Agenda

1. Organisatorial progress update by Melanie.
2. Topics of promising future research direction.
3. Next steps of Melanie.

Discussion regarding Recycling Lab St. Michael

• wait for E-Mail from Alexia Tischberger-Aldrian (visit in St. Michael)
• currently no data available
• server is set up to collect data
• various sensors record the data
• Infos regarding data – see Project Manual p. 16 chapter 7.1

Discussion regarding future publications

1. Publication
– publicate the collected and labeled data from St. Michael
– generate a GUI for labeling the data 
– the labeling is made by study assistants (about 200.000 Images)

2. Publication
– Conference Paper about Particle Tracking
– Train a network on the basis of the first Publication

eventually 3. Publication Transfer Learning
– use Open source Data for training

Main Question: How does a network learn an efficient representation to be able to build a reasonable model (even with a small amount of training data)?

To Do:

• wait for E-Mail from Alexia Tischberger-Aldrian to visit the Recycling Lab
• Ask for Waste-RL Dataset from ‘Waste Management’ Paper (www.elsevier.com/locate/wastman) – Images from Waste-RL are not very suitable
• maybe find some other Dataset instead of Waste-RL
•  Read Paper Waste Management
• Use Paper ‘Sparse Detr…’ on the Steel Defect Dataset from the Master Thesis

Supervisor: Univ.-Prof. Dr Elmar Rückert, Niko Feith
Start date: 1st March 2023

Theoretical difficulty: low
Practical difficulty: mid

Abstract

The FANUC CRX-10iA robot arm is a compliant system that can be used for collaborative human-robot tasks. 

In this Bachelor thesis, the abilities of the robot arm are evaluated for such co-worker scenarios. In particular, the reachable space, the robustness of the inverse kinematics, the ability to simulate the system in real-time, and the precision and reliability of the system are analyzed.

To embed the system in our CPS Hub, a ROS2 interface will be developed und used for all experiments. The interface can be used to control the system or to send end receive commands from simulation tools like CoppeliaSim or Gazebo. 

Tentative Work Plan

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

• Literature research
• ROS2 interface implementation for the simulation tool
• ROS2 interface implementation for the real system
• Identification of quality measures and definition of the experiments
• Evaluation 
• Thesis writing

Thesis

Development of a ROS2 Interface for the FANUC CRX-10iA robot arm

Comparative analysis of traditional map-based and mapless
navigation via deep reinforcement learning in dynamic environments

Comparative analysis of traditional map-based and mapless
navigation via deep reinforcement learning in dynamic environments

Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 5th September 2022

Theoretical difficulty: mid
Practical difficulty: mid

Abstract

For over 20 years today, the simultaneous localisation and mapping (SLAM) approach has been widely used to achieve autonomous navigation objectives. The SLAM problem is the problem of building a map of the environment while simultaneously estimating the robot’s position relative to the map given noisy sensor observations and a series of control data.  Recently, the 

mapless-based approach with deep reinforcement learning has been proposed. For this approach, the agent (robot) learns the navigation policy given only sensor data and a series of control data without a prior map of the task environment. In the context of this thesis, we will evaluate the performance of both approaches in a crowded dynamic environment using our differential drive open-source open-shuttle mobile robot.

Tentative Work Plan

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

• Literature research and a general understanding of the field
  • mobile robotics and industrial use cases
  • Overview of map-based autonomous navigation (SLAM & Path planning)
  • Overview of mapless-based autonomous navigation approach with deep reinforcement learning
  •  
• Setup and familiarize with the simulation environment
  • Build the robot model (URDF) for the simulation (optional if you wish to use the existing one)
  • Setup the ROS framework for the simulation (Gazebo, Rviz)
  • Recommended programming tools: C++, Python, Matlab
  •  
• Intermediate presentation:
  • Presenting the results of the literature study
  • Possibility to ask questions about the theoretical background
  • Detailed planning of the next steps
  •  
• Define key performance/quality metrics for evaluation:
  • Time to reach the desired goal
  • Average/mean speed
  • Path smoothness
  • Obstacle avoidance/distance to obstacles
  • Computational requirement
  • success rate
  • e.t.c
  •  
• Assessment and execution:
  • Compare the results from both map-based and map-less approaches on the above-defined evaluation metrics.
  •  
• Validation:
  • Validate both approaches in a real-world scenario using our open-source open-shuttle mobile robot.
  •  
• Furthermore, the following optional goals are planned:
  • Develop a hybrid approach combining both the map-based and the map-less methods.
  •  
• M.Sc. thesis writing
• Research paper writing (optional)

Related Work

[1] Xue, Honghu; Hein, Benedikt; Bakr, Mohamed; Schildbach, Georg; Abel, Bengt; Rueckert, Elmar, “Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics“, In: Applied Sciences (MDPI), Special Issue on Intelligent Robotics, 2022.

[2] Han Hu; Kaicheng Zhang; Aaron Hao Tan; Michael Ruan; Christopher Agia; Goldie Nejat “Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain”,  IEEE Robotics and Automation Letters ( Volume: 6, Issue: 4, October 2021).

[3] Md. A. K. Niloy; Anika Shama; Ripon K. Chakrabortty; Michael J. Ryan; Faisal R. Badal; Z. Tasneem; Md H. Ahamed; S. I. Mo, “Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review”, IEEE Access ( Volume: 9), February 2021.

[4]  Ning Wang, Yabiao Wang, Yuming Zhao, Yong Wang and Zhigang Li , “Sim-to-Real: Mapless Navigation for USVs Using Deep Reinforcement Learning”, Journal of Marine Science and Engineering, 2022, 10, 895. https://doi.org/10.3390/jmse10070895

Meeting 11/05

Research

• submitted CR-VAE paper to ECAI
• Research leads:
  1. Dimensionality Collapse of Visual Representations in Reinforcement Learning
  2. Improve SwAV architecture by creating better latent space clusters with the use of Sparse Autoencoders

PhD Registration

• Signed Application
• Will hand it over to the Admissions office

M.Sc. Students/Interns

• Possible PhD position for Iye Szin
• Early June first draft presentation

ML Course

• Assignment 5

Miscellaneous

• Kleinwassertal

Meeting 25/05

Research

Literature Review

1. Dimensionality Collapse of Visual Representations in Reinforcement Learning
2. Improve SwAV architecture by creating better latent space clusters with the use of Sparse Autoencoders

 ECAI review papers

• 6 papers assigned = 16hours(2 days)/paper = 96 hours(12 days)
• More feasible to review 2 papers.
• deadline 16 June

M.Sc. Students/Interns

ML Course

• Graded up to assignment 4
• Assignment 6 out

Miscellaneous

• Course attendance: Effective Communication in Academia: Cooperating in International Projects (Module 3)
• ChatGPT plus
• Need a web camera
• Kleinwassertal
• Holiday
• Clean AC

Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: As soon as possible

Theoretical difficulty: mid
Practical difficulty: high

Abstract

Unlike the traditional mine inspection approach, which is inefficient in terms of time, terrain, and coverage, this project/thesis aims to investigate novel 3D perception and SLAM using geometric and semantic information for real-time mine inspection.

We propose to develop a SLAM approach that takes into account the terrain of the mining site and the sensor characteristics to ensure complete coverage of the environment while minimizing traversal time.

Tentative Work Plan

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

• Study the concept of 3D perception and SLAM for mine inspection, as well as algorithm development, system integration and real-world demonstration using Unitree Go1 quadrupedal robot.

• Setup and familiarize with the simulation environment:
  • Build the robot model (URDF) for the simulation (optional if you wish to use the existing one)
  • Setup the ROS framework for the simulation (Gazebo, Rviz)
  • Recommended programming tools: C++, Python, Matlab
• Develop a novel SLAM system for the quadrupedal robot to navigate, map and interact with challenging real-world environments:

  • 2D/3D mapping in complex indoor/outdoor environments

  • Localization using either Monte Carlo or extended Kalman filter

  • Complete coverage path-planning

• Intermediate presentation:
  • Presenting the results of the literature study
  • Possibility to ask questions about the theoretical background
  • Detailed planning of the next steps

• Implementation:

  • Simulate the achieved results in a virtual environment (Gazebo, Rviz, etc.)

  • Real-time testing on Unitree Go1 quadrupedal robot.

• Evaluate the performance in various challenging real-world environments, including outdoor terrains, urban environments, and indoor environments with complex structures.
• M.Sc. thesis or research paper writing (optional)

Related Work

[1]  Wolfram Burgard, Cyrill Stachniss, Kai Arras, and Maren Bennewitz , ‘SLAM: Simultaneous
Localization and Mapping’,  http://ais.informatik.uni-freiburg.de/teaching/ss12/robotics/slides/12-slam.pdf

[2]  V.Barrile, G. Candela, A. Fotia, ‘Point cloud segmentation using image processing techniques for structural analysis’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W11, 2019 

[3]  Łukasz Sobczak , Katarzyna Filus , Adam Domanski and Joanna Domanska, ‘LiDAR Point Cloud Generation for SLAM Algorithm Evaluation’, Sensors 2021, 21, 3313. https://doi.org/10.3390/ s21103313.