20.10.2022 – Innovative Research Discussion

Meeting notes on the 20th of October, 2022

Location: Chair of CPS

Date & Time: 20th October, 2022, 12:35 pm to 1:38 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.



  1. General Discussion
  2. Update on Conference Paper
  3. Do-It-Lab

General Discussion

  1.  Add dates to all the meeting notes
  2. Add publication to the home page
  3. Contact Christopher for his presentation date and update our calendar accordingly
  4. Use the Deck app to communicate updates of current, completed and yet-to-be-done tasks

Update on Conference Paper

  1.  Compare map-based and map-less indoor SLAM methods.
  2. Focus on indoor navigation.
  3. Evaluate 1 – 3 lidar-based, 1 – 3 visual-based, and 1- 3 deep learning SLAM methods.
  4. Pick up some ideas from the referenced papers in the Deck app.


  1. Organise the students into four groups
  2.  Give the students the questionnaire after the lab to fill out and submit. You could generate a barcode using the web link to the form.

Meeting on the 19th, October 2022

Location: Chair of CPS

Date & Time: 5th Oct 2022

Participants: Univ.-Prof. Dr. Elmar Rueckert, DI Nikolaus Feith, BSc



  1. Update
  2. Future Steps/News

Top 1: Update

  • Since October 12, two presentations were prepared and one was given to the chair on the ROS2 tutorial. The second one deals with the paper “VisuoSpatial Foresigt for Multi-Step, Multi-Task Fabric Manipulation” and will be given next Thursday.
  • Furthermore, a concept for the CPS HUB for the ROS2 repositories was developed and finalized with the other PhD students. 
  • Literature search on Related work to GNN in Motion Planning has been started.
  •  First ROS2 submodule of RH8D robot arm was completed. Data of force sensors at fingertips can be published.

Top 2: Future Steps/News

  • Usage of the desk app in nexttcloud was discussed
  • ROS2: Further discussion on the shared control project, usage of the tablet with android app. Continue working on the RH8D hand.
  • Topics for the Integrated Project, Bachelor’s theses and Master’s theses should be considered. What should be investigated in more detail?
  • Not only on shared control should be further investigated but also GNN.

Introduction to Productivity, Flexibility and Team Work

Increase your Productivity

Schedule your weekly tasks, meetings, courses or activities!

Increase your Flexibility

Access your files from any computer, tablet or phone!

Work as a Team

Edit together in real-time with easy sharing, and use comments, suggestions, and action items to keep things moving. Or use @-mentions to pull relevant people, files, and events into your online files for rich collaboration.

Important Links

Meeting Notes 14.10.2022


Niko, Fotis, Linus, Vedant


  • First discussion on the projects structure of CPS Hub
  • Initial plan & examples


  • Components (e.g. UR3, RH8D_hand, Glove, Hololens2) are independent repositories
  • Projects (e.g. TacProMPs, HololensTeleop) are independent repositories that use the above repos.
  • No custom messages without previous team meeting
  • Use Foxy ROS2

ROS2 Tutorial

Robot Operating System 2 (ROS2) is an open source middleware for the development of robot applications. This tutorial describes the architecture and the basic components. Furthermore, a comparison to ROS1 is drawn and the application with Python is explained in more detail.

Hier finden sie den Foliensatz zum Vortrag.

17.10.2022 – Introduction to CAD Software

Why do I need CAD Software?

  • Computer-Aided Design (CAD) is the cornerstone of how you design and build things. It allows the user to digitally create, visualise, and simulate 2D or 3D models of real-world products before it is being manufactured.
  • CAD models allow users to iterate and optimize designs to meet design intent.
  • The use of CAD software facilitates the testing of real-world conditions, loads, and constraints, which increases the quality of the product.
  • CAD software helps to explore ideas and visualise the concept.
  • Improve the quality, precision of the design, and communication in the design process.
  • Analyse real-world scenarios by computer-aided analysis
  • Create a database for product development and manufacturing.

Some Practical Applications of CAD Software

Source: https://learnsolidworks.com/
Source: https://automation.siemens.com/
Source: https://leocad.org/

Automobile parts can be modelled, visualised, revised, and improved on the screen before being manufactured.

Electrical schematics, control circuit diagrams, PCBs, and integrated circuits (ICs)  can be designed and developed with ECAD software 

With CAD software, architects can visualise and simulate their entire project using real-world parameters, without needing to build any physical structuress or models. 

What CAD software do I need?

Something free

  • FreeCAD
  •  TinkerCAD
  • Fusion 360
  • Onshape
  • Solid Edge
  • Blender
  • SketchUp

My design goes with me wherever I go (cloud-based)

  • Onshape
  • TinkerCAD
  • AutoCAD Web
  • SelfCAD
  • Vectary
  • SketchUp

Something more advanced and professional

  • AutoCAD
  • Autodesk Inventor
  • SolidWorks
  • Fusion 360
  • Solid Edge
  • Onshape
  • Shapr3D
  • Creo

Windows OS

  • AutoCAD
  • Autodesk Inventor
  • Solidworks
  • Fusion 360
  • Creo
  • Solid Edge
  • Shapr3D
  • Blender

Linux OS

  • NX Advanced Designer
  • Blender


  • AutoCAD
  • Autodesk Inventor
  • Fusion 360
  • Shapr3D
  • Blender
  • NX Advanced Designer

iOS, Android

  • AutoCAD
  • Autodesk Inventor
  • Shapr3D

Where can I learn CAD?

  1. Coursera:  https://coursera.org/courses?query=cad
  2. Udemy:  https://udemy.com/topic/autocad/
  3. MyCADSite:  https://mycadsite.com/
  4. Skill Share:    https://skillshare.com/search?query=solidworks
  5. CAD-Tutorials.de:  https://cad-tutorials.de/
  6. Youtube:  https://youtube.com/watch?v=cAgpDFTHxpY
  7. CADTutor:  http://cadtutor.net/
  8. PTC Training:  https://ptc.com/en/ptc-university/training-catalogs
  9. Autodesk Tinkercad: https://tinkercad.com/

Introduction to Python

Why should you consider learning Python

  • Python is the most popular programming language in the world with a popularity of 28%.
  • It is easier to learn than many other programming languages.
  • Python is very readable due to its structure. Thus, bugs can usually be found and fixed quickly.
  • Python can be both procedural and object-oriented, which makes it very versatile.
  • There is a large number of software libraries. On python package index, PyPI, over 400,000 packages can be found. And the most important ones can be downloaded quickly and easily using pip.
  • Python and almost all Python software libraries are available for free on the Internet.

What Python can be used for

  • Data Science is one of the most popular application areas of Python. Here, not only the complete data analysis but also the visualization will be implemented in Python. Generally, software libraries such as Numpy, Matplotlib and Pandas are used for this purpose.

  • Machine learning is another very popular area. Python can be used for supervised, unsupervised and reinforcement learning. Libraries such as TensorFlow, Keras, PyTorch or scikit-learn are used for this purpose.

Furthermore, Python can be used for all kinds of general purpose programs, such as app development, GUI design, web development and in many other areas. In addition, programs with Python interfaces can be automated through a Python program, for example simulation programs.

Practical example of Python in academia

Especially during the time as a student it is good to have a tool with which you can validate your hypotheses or perform the calculations by the computer. Thus, programs can be written with which problems in physics, chemistry or other tasks can be simulated and subsequently the simulation data can be plotted or even videos can be generated.

Important links

Understanding the basics of privacy

Important Articles

Companies like Google collect and process your data

Google collects your data from many different sources. Here are some examples:

  • Gmail: Google can read and store information from every email you write and receive, including in the spam, draft, and trash folders.
  • Google Maps: Google saves every location you search, in addition to all the places you physically visit with your devices, even if you aren’t logged in. Are you using Waze instead? Google owns that too. The ubiquity of phones and our constant use of them makes them almost like tracking devices we carry around willingly.
  • Android devices: Because Android phones and tablets run on an operating system built by Google, the company can track which ads you’re shown while using your phone. Google also knows what time, down to the second, you open each app.
  • Google apps: The Google Play store records all your searches and downloads, as well as any rewards cards used. Google also tracks which articles you’ve read through Google News.
  • YouTube: Google acquired YouTube back in 2006. When you’re using YouTube, Google tracks your search history, your watch history, how long you spend watching videos, and all your comments and likes or dislikes.
  • Google Assistant: Every request you make and every question you pose is recorded — you can even listen to the audio playback.
  • G Suite: Your calendar shows where you’ll be and when, and Google Hangouts saves all of your conversations.

If you are interessted in which data Google has collected about you, test Google Takeout.

Recommendations: Browser, Search Engine & Online Docs

In our digital age, we have to be aware of the data collection strategies of all services that we use. However, often, alternatives  developed by the open-source community exist. Here are some recommendations:

Recommendations: Messenger & Repositories

  • I personally recommend: Nextcloud’s Talk App.
  • Setup your own repo server using, e.g., Gitea or Gitey.

Final remarks: Stay sensitive to what happens to your data. Nothing is for free.

07.10.2022 – Innovative Research Discussion

Meeting notes on the 4th of October, 2022

Location: Chair of CPS

Date & Time: 7th October, 2022, 09:15 am to 10:25 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.


  1. Discussion on  ROS-Mobile Control
  2. Discussion on ODrive torque control

ROS-Mobile and ODrive torque control

  1.  Re-implement the o2s control approach to accommodate the information in the attached figure.
  2.  Write the Arduino code taking into account the rotation matrices
  3. Implement the open-loop torque control approach

Digital Competencies – Learning Python and some Mathematics

Getting started with Python

This tutorial gives an instruction on installing Cuda and enabling Cuda acceleration using Pytorch in Win10. Installation in Linux or Mac systems are all possible. An additional .py file will verify whether the current computer configuration uses the Cuda or not. The following instruction assumes that you have already installed Python IDE, e.g., Anaconda, Pycharm, Visual Studio…

Step 1: Check which Cuda version is supported by your current GPUs under this website. From the left figure, we can see that A100 supports Cuda 11.0. It is also reported from other blogs/ forums that A100 can support Cuda 11.1. In this post, we install Cuda 11.1.

Step 2: Download Nvidia Cuda Toolkit 11.1 (the same version as Cuda in Step 1) from the website. In Win10, for instance, we follow up the choice as shown right. The size of exe(local) is around 3.1GB. After downloading, run the .exe and perform installation. It may take some minutes to complete installation.

Step 3: On the homepage of Pytorch, choose the appropriate options as shown in the left figure. IMPORTANT: The cuda version must be the same as in Step 1. It is also recommended to use Stable version. After finishing the , copy the command into Anaconda Powershell Prompt or other command prompt where you install packages for Python. Waiting for the installation, which may require larger than 1GB disk space and takes some minutes for installation. You could also find historical version of Pytorch in that homepage.

Verify your installation with .py file

You could download a cuda-test.py file and run it. If the result shows ‘cuda’, then you can enjoy the Cuda acceleration for training neural networks!

Using Multiple GPUs for further acceleration

Running Pytorch with Multiple GPUs can further increase the efficiency. We have 8 GPU cards and can be used parallely for training. Please refer to (1) (2) (3) for details.