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FANUC CRX-10iA

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The FANUC CRX-10iA robot is a six-axis robot that has a reach of 1249 mm and can carry up to 10 kg of weight. The reliability and sensitive touch detection allows the CRX-10iA to work safely alongside humans in a variety of industrial and manufacturing tasks.

Links

Videos

  • Research videos using the robot will be presented here. 

Publications

  • Publications about the robot as well as related topics will be found here.


Open Source Mobile Robot

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CPS presents a guide to build an open-source modular mobile platform for research, navigation and logistics applications. Its design leveraged off-the-shelf (OTS) components, additive manufacturing technologies, aluminium profiles, and consumable hover-board high-torque brushless direct current (BLDC) motors. It is compatible with the robot operating system (ROS) with a maximum payload of 90kg.

It provides a simple yet robust framework for contextualising simultaneous localisation and mapping (SLAM) algorithms, which are the fundamental prerequisites for autonomous navigating robots. It features several control approaches such as remote control from RC devices, Android-based devices, hand gestures, ROS rqt etc.

Components

Sensors:

  • RPLIDAR A2 M8

  • RealSense Depth Camera D435i

  • Intel Realsense Tracking Camera T265

  • Turnigy 9X 2,4 GHz 8CH Receiver (V2)

Power Supply:

  • VARTA Slim Power Bank – 18000 mAh

  • 36V Lithium-ion battery 4400mAh

Electronics:

  • 2 x 6,5“  hoverboard brushless DC (BLDC) motors with 15 pole pairs

  • Nvidia Jetson Nano B01 64GB

  • Arduino Mega 2650 Rev3

  • ODRIVE 56V V3.6

Videos

Publications

More information can be found in the publication: https://doi.org/10.1016/j.ohx.2023.e00426

Links



RMP Lite 220

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Picture Source: https://robotics.segway.com/ [visited at 15.07.2022]

The RMP Lite 220 is a highly adaptable robot which can be used for different scenarios like f.e. indoor/outdoor delivery, patrolling, service, cleaning, AFV (warehouse), or other special applications.

Links

Videos

  • Research videos using the robot will be presented here. 

 

Publications

  • Publications about the robot as well as related topics will be found here.


M.Sc. Thesis: Nikolaus Feith on A Motor Control Learning Framework for Cyber Physical Systems

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Supervisor: Univ.-Prof. Dr Elmar Rückert
Start date: 1st of July 2021

Theoretical difficulty: mid
Practical difficulty: mid

Abstract

 A central problem in robotics is the description of the movement of a robot. This task is complex, especially for robots with high degrees of freedom. In the case of complex movements, they can no longer be programmed manually. Instead, they are taught to the robot utilizing machine learning. The Motor Control Learning framework presents an easy-to-use method for generating complex trajectories. Dynamic Movement Primitives is a method for describing movements as a non-linear dynamic system. Here, the trajectories are modelled by weighted basis functions, whereby the machine learning algorithms must determine only the respective weights. Thus, it is possible for complex movements to be defined by a few parameters. As a result, two motion learning methods were implemented. When imitating motion demonstrations, the weights are determined using regression methods. A reinforcement learning algorithm is used for policy optimization to generate waypoint trajectories. For this purpose, the weights are improved iteratively through a cost function using the covariance matrix adaptation evolution strategy. The generated trajectories were evaluated in experiments. 

Thesis Document

A Motor Control Learning Framework for Cyber-Physical-Systems



Data Science Summer School 2022, Leoben

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Univ.-Prof. Dr. Elmar Rueckert presents in his two slots how machine learning approaches can be used in robotics. 

Link to the slides on

Materials for the Robotics Workshop


Additional materials:



Clemens Fritze, B.Sc.

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Student Assistant at the Montanuniversität Leoben

Foto_20220621

Short bio: Clemens Fritze, B.Sc B.Sc started at CPS in July 2022. After a short break he joined the team again in November 2023.

Clemens Fritze is a master student at the Montanuniversität Leoben. Prior to his master program he studied Mechanical Engineering at the Montanuniversität Leoben, where he passed his Bachelor defense in May 2022. In 2018, Mr. Fritze finished a Bachelor study in Business Informatics at the university of applied science in Vienna (german FH Technikum Wien).

Research Interests

  • Robotics
  • Applied Machine Learning
  • IoT

Thesis

Contact

Clemens Fritze, B.Sc B.Sc.
Student Assistent at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Email:   clemens.fritze@stud.unileoben.ac.at



qoncept dx GmbH / Leoben

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Hybride Modellierung Metallurgischer Prozesse

Contact

  • qoncept dx GmbH
  • Peter Tunner-Straße 19, 8700 Leoben
  • office@qoncept.at
  • Web: https://qoncept.at/

Laufende Projekte, Bachelor- und Masterarbeiten



M.Sc. Thesis: Daniel Wagermaier: Predictive Models for Copper Content and Energy Consumption in Electric Arc Furnace Operations

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Supervisor: Univ.-Prof. Dr Elmar Rückert, Qoncept GmbH
Start date: 1st of August 2022

Theoretical difficulty: mid
Practical difficulty: low

Introduction

As direct observations and permanent measurements during steelmaking processes are not possible, modelling has become a powerful tool. The technique of fundamental-based metallurgical modelling is well-established and demonstrates its capabilities in a wide range of applications in modern steelmaking. Following the general trend, data-driven approaches are increasingly used today in various areas of metallurgical modelling, in addition to these  classical fundamental approaches. Depending on the field of application, fundamental-based and data-driven models both have their own advantages and disadvantages.

The overall goal of the present thesis is to combine both models in order to leverage the strengths of  these two different methods. The first step is to apply several different data-driven models and compare them to the metallurgical model to see how they perform differently. In the second phase, various ways of combining data-driven models with the metallurgical model should be investigated. For example, this could be done via a data-driven optimization of its tuning parameters or by replacing them with data-driven models. Also, adding a data-driven residual term to the metallurgical model could be possible. Based on these findings, the third part of the thesis should focus on online learning and methods of how to avoid an off-drifting of the model. The fourth and last section of the thesis should investigate various ways of detecting errors in new data. While point one and two are the main focus of the thesis, point three and four are considered to be optional.

Tentative Work Plan

The following concrete tasks will be focused on:

  • Literature research.
  • Training of different data-driven models in Python.
  • Performance comparison between data-driven models and the metallurgical model.
  • Combination of selected data-driven models and the metallurgical model in Python.
  • (Optional) Investigate different ways for online learning and live performance evaluation.
  • (Optional) Anomaly detection in new data.
  • Thesis writing.

Thesis Document



B.Sc. or M.Sc. Thesis/Project: Simultaneously predicting multiple driving strategies using probabilistic inference

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Supervisors: Univ.-Prof. Dr Elmar Rückert, LUPA Electronics GmbH
Start date: ASAP from June 2022

 

Theoretical difficulty: high
Practical difficulty: low

Abstract

Wir Menschen sind in der Lage unter widrigen Bedingungen z.B. bei eingeschränkter Sicht, oder bei Störungen komplexe Vorgänge wahrzunehmen, vorherzusagen und innerhalb von wenigen Millisekunden zusammenhängende Entscheidungen zu treffen. Mit dem zunehmenden Grad der Automatisierung steigen auch die Anforderungen an künstliche Systeme. Immer komplexere und größere Datenmengen müssen verarbeitet werden um autonome Entscheidungen zu treffen. Mit gängigen KI Ansätzen stoßen wir aufgrund der konvergierenden Miniaturisierung an Grenzen, die z.B. im Bereich des autonomen Fahrens nicht ausreichen, um ein sicheres autonomes System zu entwickeln.

Ziel dieser Forschung ist es probabilistische Vorhersagemodelle in massiv parallelisierbaren neuronalen Netzen zu implementieren und mit diesen komplexe Entscheidungen Aufgrund erlernter interner Vorhersagemodelle zu treffen. Die neuronalen Modelle verarbeiten hoch dimensionale Daten moderner und innovativer taktiler und visueller Sensoren. Wir testen die neuronalen Vorhersage und Entscheidungsmodelle in humanoiden Roboteranwendungen in dynamischen Umgebungen.

Unser Ansatz beruht auf der Theorie der probabilistischen Informationsverarbeitung in neuronalen Netzen und unterscheidet sich somit grundlegend von den gängigen Methoden tiefer neuronaler Netze. Die zugrundeliegende Theorie ermöglicht weitreichende Modelleinblicke und erlaubt neben den Vorhersagen von Mittelwerten auch Unsicherheiten und Korrelationen. Diese zusätzlichen Vorhersagen sind entscheidend für verlässliche, erklärbare und robuste künstliche Systeme und sind eines der größten offenen Probleme in der künstlichen Intelligenz Forschung.

Tentative Work Plan

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

  • Literature research on graphical model inference of motion plans.
  • Toy Task implementation in Python. 
  • Implementation of  GMMs, PTSMs and combinations in Python.
  • Visualization and analysis of the prediction performance. Definition of suitable evaluation criteria.
  • (Optional) Implementation in a realistic driving simulator.
  • Analysis and evaluation of the generated data.


LUPA-Electronics GmbH / Berlin

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Sicheres Autonomes Fahren mit Probabilistischen Neuronalen Netzen

Contact

Wir Menschen sind in der Lage unter widrigen Bedingungen z.B. bei eingeschränkter Sicht, oder bei Störungen komplexe Vorgänge wahrzunehmen, vorherzusagen und innerhalb von wenigen Millisekunden zusammenhängende Entscheidungen zu treffen. Mit dem zunehmenden Grad der Automatisierung steigen auch die Anforderungen an künstliche Systeme. Immer komplexere und größere Datenmengen müssen verarbeitet werden um autonome Entscheidungen zu treffen. Mit gängigen KI Ansätzen stoßen wir aufgrund der konvergierenden Miniaturisierung an Grenzen, die z.B. im Bereich des autonomen Fahrens nicht ausreichen, um ein sicheres autonomes System zu entwickeln.

Ziel dieser Forschung ist es probabilistische Vorhersagemodelle in massiv parallelisierbaren neuronalen Netzen zu implementieren und mit diesen komplexe Entscheidungen Aufgrund erlernter interner Vorhersagemodelle zu treffen. Die neuronalen Modelle verarbeiten hoch dimensionale Daten moderner und innovativer taktiler und visueller Sensoren. Wir testen die neuronalen Vorhersage und Entscheidungsmodelle in humanoiden Roboteranwendungen in dynamischen Umgebungen.

Offende Projekte, Bachelor- und Masterarbeiten​