Mechatronics/Robotics: Curriculum

1. Semester

Name ECTS
SWS
Module 1 (MOD1 )
German / iMod
6.00
-
Engineering mathematics (BIWM)
German / ILV
6.00
4.00

Course description

This course deals with mathematical topics and their applications in mechatronics.

Learning outcomes

After passing this course successfully students are able to ...

  • model and solve simple problems from statistics and probability theory using methods from combinatorics and calculus
  • solve simple statistical estimation problems
  • model and solve simple geometrical problems in R² and R³ using vectors
  • describe and solve systems of linear equations in the framework of linear algebra
  • apply methods from linear algebra in order to decompose matrices and to represent linear functions by matrices
  • describe, interpret and calculate the singular value decomposition as well as the pseudoinverse of a matrix

Course contents

  • part I: linear algebra
  • part II: combinatorics, probability theory and statistics

Prerequisites

Mathematics and robotics (bachelor level), in particular- vector spaces, matrices, elementary functions and complex numbers- sequences, (power) series, limits, differential und integral calculus- Fourier series, Fourier transformation, Laplace transformation- multivariate calculus and differential equations

Literature

  • Teschl G./ Teschl S. (2008), Mathematik für Informatiker, Band 1: Diskrete Mathematik und Lineare Algebra, 3rd ed. Springer
  • Teschl G./ Teschl S. (2007), Mathematik für Informatiker Band 2: Analysis und Statistik, 2nd ed. Springer
  • Stingl, P., Mathematik für Fachhochschulen, 2009, Hanser
  • Sachs, M., Wahrscheinlichkeitsrechnung und Statistik für Ingenieurstudenten an Fachhochschulen, Hanser
  • Lang, Ch./ Pucker, N. (2005): Mathematische Methoden in der Physik, 2. Auflage, Spektrum

Assessment methods

  • Problem sessions and written exam
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

Problem sessions:- Self-study exercises must be solved and entered in the „checkmark-list“ (possible until 7:00 am, day of the problem session course). During the problem sessions exercises must be presented and explained at the Whiteboard- At least 60% of the given examples must be solved and checked in the list. Grading of the exercise part: 60% - 70% - 4 70% - 80% - 3 80% - 90% - 2 90% - 100% - 1 Written exams: Each of the two exams can be repeated once. In case of a commissional exam the whole content of the course is to be examined. Final grading: the exercise part contributes with 20% to the final grade. The two exams contribute 40% each. In case of a failed exercise part the two exams count 50% each for the reoeat exam.

Module 2 (MOD2)
German / iMod
6.00
-
Modern Programming Concepts (BMPK)
German / ILV
6.00
4.00

Course description

A modern approach to object oriented progamming and artificial intelligence.

Learning outcomes

After passing this course successfully students are able to ...

  • apply basic concepts of objectoriented programming methods like: - classes - operator overloading - composition and inheritance - templates and iterators - container-classes
  • describe agents and environments
  • name the concept of rational behaviour
  • distinguish between different problem environments.
  • distinguish between agent structures
  • name problem-solving agents
  • design a kind of goal-based agent
  • distinguish between problem types
  • do a graph search with partial information
  • find a defined problem formulation
  • describe basic search algorithms including: - uninformed search strategies - constraint satisfactory search - informed search strategies
  • describe and define the first heuristic functions for the informed search
  • apply simulated annealing techniques
  • apply evolutionary algorithms

Course contents

  • the class concept in object-oriented programming
  • operator overloading
  • inheritance
  • templates and iterators
  • vectors and container-classes
  • agents
  • problem solving
  • informed search
  • constrain satisfactory problems
  • games

Prerequisites

Mathematics, structured programming.

Literature

  • Hubbard, John, Schaum's Outline of Programming with C++, McGraw-Hill, ISBN-10: 3826609107, ISBN-13: 978-3826609107
  • Russell, Stuart/ Norvig, Peter, Künstliche Intelligenz, Pearson, ISBN: 978-3-8689-4098-5

Assessment methods

  • Written exam
  • Project
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

basic programming skills are essential to pass this course; please visit an adequate warm-up programming course if needed!

Module 3 (MOD3)
German / kMod
6.00
-
Electronic (BEL)
German / ILV
6.00
4.00
Module 4 (MOD4)
German / iMod
6.00
-
Industrial Robotics (BIRB)
German / ILV
3.00
2.00

Course description

This course covers issues related to the development, design and dimensioning of industrial robots (IR) (e.g., composition, kinematics, dynamics, trajectory planning and simulation).

Learning outcomes

After passing this course successfully students are able to ...

  • enumerate the components of an IR and describe their interacting
  • explain essential control concepts of an IR
  • solve non-linear equation within the field of robotics with numerical methods
  • describe the kinematics of a robot with the DH-method
  • use the inverse kinematics concept in order to calculate the joint parameters due to a given movement
  • calculate the forces and torques of the servomotors according to a given trajectory and subsequently select a servomotor from a catalogue
  • simulate the behaviour of a given manipulator using a model in MatLab and linearize the model in given points
  • compose a description of a given robot using formal mathematical models
  • calculate the offset of the TCP due to limited stiffness and load
  • calculate inverse and direct kinematics and to simulate dynamics of parallel mechanisms.

Course contents

  • development phases and calculation methods for design and optimization of robots and robot subsystems (kinematic structures, transmissions, drives, ...)
  • robot kinematics (parallel and serial)
  • in-depth study of parallel kinematics
  • fundamentals of position description (Euler, quaternions, ...)
  • forward and reverse transformations
  • robot dynamics
  • Lagrange-Euler formulation
  • control algorithms
  • trajectory control strategies and programming
  • setting up simulation models of serial robots in MatLab
  • modelling non stiff robots
  • simulate non stiff robots under deflection and distortion of bodies and axes
  • Linearisation of the offset of the TCP, in order to define for corrective actions within feedback control systems.

Prerequisites

- engineering science fundamentals- fundamentals of mechatronics- fundamentals of robotics, industrial Robotics- mechatronic systems- design of robots (will be partially repeated)

Literature

  • Spong, M., Hutchinson, S., Vidyasagar, M. (2006), Robot Modeling and Control. Wiley & Sons; Auflage 1 ISBN-10: 0471649902
  • Sciavicco, L., Siciliano, B., (2005), Modelling and Control of Robot Manipulators; Springer
  • John, J. C., 2003. Introduction to robotics - Mechanics and control second edition; Addison Wesley Publishing Company ISBN 0-201-09528-9
  • Murray, Richard M., 1994. A mathematical introduction to robotic manipulation Verlag: Crc Pr Inc; Auflage: 0002 ISBN-10: 0849379814
  • Brillowski, K., 2004. Einführung in die Robotik. Auslegung und Steuerung serieller Roboter. Shaker Verlag ISBN-10: 3832234160

Assessment methods

  • Comprehensive grade (project report of a non-stiff robot, MatLab Simulation), presentation
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Mechatronics 1 (BMECH)
German / ILV
3.00
2.00
Module 5 Intelligent Manufacturing (MOD5)
German / kMod
6.00
-
Advanced Sensor Systems (BASS)
German / ILV
2.00
1.00

Course description

Advanced principles in sensor technology, main focus on 2D/3D image-based sensor systems with applications in automation & robotics

Methodology

Lecture and practical exercises

Learning outcomes

After passing this course successfully students are able to ...

  • to define and explain sensor concepts of advanced imaging sensors like color camera, infrared camera, ToF, stereo or LIDAR.
  • to explain the signal path from the dynamic scene, acquisition, preprocessing, feature extraction through to the scene interpretation and to discuss and evaluate the learned methods.
  • to apply methods learned using own MatLab and C/C++ programs to robotic tasks and evaluate and compare them by simulation.
  • to define and explain the relationships and differences between data mining and machine learning.

Course contents

  • Motivation
  • Sensor concepts on image-based sensors (2D/3D)
  • Calibration, registration and fusion
  • Feature extraction, scene interpretation
  • Data mining and machine learning
  • Applications in automation & robotics

Prerequisites

- Mathematics - Electrical engineering - Sensor technology and metrology - Signal and image processing

Literature

  • P. Azad, T. Gockel, R. Dillmann, Computer Vision - Das Praxisbuch, Elektor-Verlag, 2007
  • W. Burger, M.J. Burge, Digitale Bildverarbeitung, Springer, 2005.
  • G. Bradski, A. Kaehler, Learning OpenCV, O`Reilly, 2008.
  • B. Jähne, Digitale Bildverarbeitung, 6. Auflage, Springer, 2005.
  • R. Siegwart, I.R. Nourbakhsh, D. Scaramuzza, Autonomous Mobile Robots, 2. Edition, MIT Press, 2011.

Assessment methods

  • Course immanent assessment method, examination, exercises
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

none

Intelligent Manufacturing Systems (BIMS)
German / ILV
2.00
1.00

Course description

In the course the students get to know fundamental principles, functionalities, operating modes and methods of smart manufacturing; furthermore the course covers the application of smart manufacturing concepts by means of “intelligent” manufacturing systems or concepts like a “digital factory”. Furthermore the course gives an overview about relevant engineering processes, methods and tools.

Learning outcomes

After passing this course successfully students are able to ...

  • explain impacts, potentials, challenges and risks of industrial digitalisation and to substantiate these concepts for a specific production setting
  • describe concretely how typical production entities (worker, machine, product, tool, etc.) become „intelligent“ within smart manufacturing concepts, herewith enabling entrepreneurial prospects and challenges
  • combine innovative technologies (e.g., internet of things, software agents, collaborative and cognitive robotics, data analytics, point-to-point communication between humans and machines, etc.) with strategic business models and concepts (e.g., mass customization, modularisation, adaptive and resilient production, digital factory, business process virtualisation, etc.) towards a smart manufacturing concept
  • list risks and development areas of intelligent manufacturing concepts – in particular concerning security, safety and reliability, interoperability, energy management, human-machine collaboration, usability, data modeling
  • explain reference models and architectures concerning intelligent manufacturing and subsequently to design concrete solutions for practical applications
  • understand and describe necessary development processes for intelligent production systems and understand methods and tools for systems engineeering and their use for mechatronic product development

Course contents

  • Impacts, potentials, challenges and risks of industrial digitalisation
  • Intelligence of typical production entities (worker, machine, product, tool, etc.) within smart manufacturing concepts
  • Innovative technologies (e.g., internet of things, software agents, collaborative and cognitive robotics, data analytics, point-to-point communication between humans and machines, etc.) as driver of business concepts (e.g., mass customization, modularisation, adaptive and resilient production, digital factory, business process virtualisation, etc.)
  • Risks and development areas of intelligent manufacturing concepts (security, safety and reliability, interoperability, energy management, human-machine collaboration, usability, data modelling)
  • Reference models and architectures concerning intelligent manufacturing and subsequent solution design
  • Systems Engineering development processes, methods and tools

Prerequisites

Fundamental knowledge in the fields of production management, production engineering, informatics, automatics

Literature

  • Brauckmann, O. (2014), Smart Production: Wertschöpfung durch Geschäftsmodelle, Springer
  • Porter, M., Heppelmann, J. (2014), Wie smarte Produkte den Wettbewerb verändern, HBM Sonderdruck 12/2014, Harvard Business Publishing
  • Porter, M., Heppelmann, J. (2015), Wie smarte Produkte Unternehmen verändern, HBM Sonderdruck 12/2015, Harvard Business Publishing

Assessment methods

  • course-immanent assessment
  • written final exam
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

none

Leading of project teams (BFPT)
German / SE
2.00
2.00

Course description

In the course the students get to know main principles of leading teams.

Learning outcomes

After passing this course successfully students are able to ...

  • explain the role of leadership in the different stages of team development (for example by Tuckman) and to derive relevant leading actions (for example directive leadership in the forming phase).
  • diagnose dynamics in project teams using models (for example Rank Dynamics, Drama Triangle, TZI) and to develop and argue case-related concrete opportunities for activities (for example delegation of responsibilty, critical discussion).

Course contents

  • Leadership styles and actions (in leading projects teams)
  • Leadership tools in project teams
  • Consequences of not leading
  • Role conflicts "colleague" and "project leader"
  • Conflicts and difficult situations in leading project teams

Literature

  • Cronenbroeck, Wolfgang (2008): Projektmanagement, Verlag Cornelsen, Berlin
  • DeMarco, Tom (1998): Der Termin – Ein Roman über Projektmanagement, München: Hanser
  • Kellner, Hedwig (2000): Projekte konfliktfrei führen. Wie Sie ein erfolgreiches Team aufbauen, Hanser Wirtschaft
  • Majer Christian/Stabauer Luis (2010): Social competence im Projektmanagement - Projektteams führen, entwickeln, motivieren, Goldegg-Verlag, Wien

Assessment methods

  • Case study (grade)
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

none

2. Semester

Name ECTS
SWS
Module 10 (MOD10bb)
German / kMod
6.00
-
International project management (BIPM)
English / ILV
3.00
2.00

Course description

This course is an introduction to project portfolio management and to international project management. It explains some of the critical success factors of managing projects in heterogeneous project portfolio environments and in international projects – especially those that are not present or perhaps not that critical in a domestic project.

Learning outcomes

After passing this course successfully students are able to ...

  • identify, categorize, evaluate, select and prioritize the components of a project portfolio
  • identify and analyze risks in a project portfolio and develop responses to risks in a project portfolio
  • balance a project portfolio regarding the relevant indicators of a concrete company scenario, e.g., time patterns and schedule, regional balance, target market priorities, utilization of resources, etc.
  • monitor and control project portfolio performance
  • evaluate the influence of culture on international projects
  • explain the necessity of different management styles in international projects
  • analyze team management according to specific situations in international projects including the ability to define adequate counter-actions
  • explain the challenges of collaboration in virtual teams
  • identify communication problems in international projects including the ability to define adequate counter-actions
  • suggest appropriate solutions to diverse problems in international projects

Course contents

  • differences between projects, project portfolios and programs
  • program management and multi project management
  • responsibilities in programs and project portfolios
  • roles in project portfolios
  • stakeholders of project portfolios
  • project portfolios and strategy
  • processes of project portfolio management
  • what is culture?
  • why do international projects fail?
  • leadership in international projects
  • multicultural teams in projects
  • managing virtual project teams
  • international communication

Prerequisites

Basic Knowledge of Project Management.

Literature

  • Adler, Nancy J., Gundersen, Allison (2007): International Dimensions of Organizational Behavior, 5th edition, Ohio: Thomson South-Western
  • Binder, Jean (2007): Global Project Management: Communication, Collaboration and Management Across Borders, Farnham: Ashgate Publishing
  • Hofstede, Geert, Hofstede, Gert Jan (2004): Culture and Organizations: Software of the Mind, 3rd edition, New York: McGraw-Hill
  • Hofstede, Gert Jan, Pedersen, Paul B., HOofstede, Geert (2002): Exploring Culture: Exercises, Stories and Synthetic Cultures, Boston: Nicholas Brealey
  • Köster, Kathrin (2009): International Project Management, London: Sage
  • Lientz, Bennet, REA, Kathryn (2002): International Project Management, San Diego: Academic Press
  • Lomnitz, Gero (2008): Multiprojektmanagement. Projekte erfolgreich planen, vernetzen und steuern, 3. Auflage, Frankfurt am Main: Moderne Industrie
  • Solomon, Charlene, Schell, Michael (2009): Managing Across Cultures: The Seven Keys to Doing Business with a Global Mindset, New York: McGraw-Hill

Assessment methods

  • Course immanent assessment method and end exam Case studies, contribution in class and end exam
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

Teaching language is English

Planning and controlling (BPUC)
German / ILV
3.00
2.00

Course description

The main focus of this lecture lies in understanding and applying the most important controlling tools (e.g. SWOT-analysis, investment appraisal methods, cost accounting, budgeting, reporting etc.).

Methodology

lecture, exercises

Learning outcomes

After passing this course successfully students are able to ...

  • to describe the different tasks of controlling
  • differentiate between strategic and operating controlling
  • to apply selected controlling-tools
  • analyse deviations between planned and realised results

Course contents

  • management
  • controlling
  • financial accounting
  • strategic planning and control
  • investment planning
  • cost accounting and production program planning
  • budgeting and deviation analysis
  • risk controlling
  • value controlling
  • reporting

Prerequisites

business administration basics

Literature

  • Wala/Groblschegg (2016): Kernelemente der Unternehmensführung, Linde-Verlag.
  • Eisl/Hofer/Losbichler (2015): Grundlagen der finanziellen Unternehmensführung. Band IV: Controlling, 3. Auflage, Linde-Verlag.
  • Schultz (2015): Controlling. Das Basiswissen für die Praxis, 2. Auflage, dtv-Verlag.

Assessment methods

  • final written exam (100%)
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

Further information regarding this course as well as accompanying materials (e.g. slides, exercise book etc.) can be found on moodle.

Module 6 (MOD6bb)
German / kMod
6.00
-
Additive Manufacturing (BGFE)
German / ILV
3.00
2.00

Course description

The course explains all common additive manufacturing technologies including their advantages and disadvantages. Within practical exercises, the complete process chain of additive manufacturing – starting with the digitalisation of physical models up to the production of additively manufactured parts – is done practically

Methodology

Lecture and hands-on lab work

Learning outcomes

After passing this course successfully students are able to ...

  • select a suitable method for a specific application.
  • identify the advantages and disadvantages of different methods and to balance them among each other according to the requirements of a certain application
  • manufacture components

Course contents

  • material properties
  • additive manufacturing
  • introduction and overview
  • rapid prototyping systems
  • liquid photopolymers
  • powder-based methods
  • solids
  • method comparison
  • data processing and software
  • reverse engineering

Prerequisites

keine

Literature

  • Abts, G. (2014): Kunststoff-Wissen für Einsteiger, Hanser
  • Gebhart, A. (2007): Generative Fertigungsverfahren, Rapid Prototyping – Rapid Tooling – Rapid Manufacturing, Hanser

Assessment methods

  • 50% lecture, 50% lab work. Both parts need to be positive in order for you to pass.
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Computer Aided Engineering (BCAE)
German / ILV
3.00
2.00

Course description

The course describes the methods of computer aided engineering (CAE) and especially the finite element method (FEM), and guides the students to perform a self-chosen FE simulation project.

Learning outcomes

After passing this course successfully students are able to ...

  • define CAE and explain relevant CAE methods including application purpose and practical application scenarios
  • explain the fundamentals of FEM analysis and illustrate application examples
  • able to perform an own FE-simulation project using SolidWorks Simulation or Nastran

Course contents

  • CAE overview
  • simulation chaining
  • FEM simulation workflow
  • FEM basics
  • element types (integration points, functions)
  • differences CAD-integrated versus stand-alone FEM tools
  • simulation in product development
  • result evaluation
  • result interpretation
  • accuracy governing factors
  • discretization rules
  • prerequisites for realistic simulation

Prerequisites

Modeling, mechanics, material science, (fluid mechanics)

Literature

  • Um, D. (2015), Solid Modeling and Applications: Rapid Prototyping, CAD and CAE Theory, Springer

Assessment methods

  • course-immanent assessment and final oral exam
Module 7 (MOD7bb)
German / kMod
6.00
-
Advanced Automation (BAAU)
German / ILV
3.00
2.00

Course description

During the course students should apply, extend and improve their ability to model, control and simulate a mechatronic system (e.g., a car or a robot)

Learning outcomes

After passing this course successfully students are able to ...

  • define and explain terms of advanced control technology as state controllers, Riccati controllers, state observers, Kalman filter and robust and nonlinear control
  • represent a system to be controlled in state space and design a state observer and a state controller according to different – also robust – quality criteria
  • convert non-linear systems by linearization in a linear state space representation and design a state space controller for it
  • apply the learned methods with MatLab/ Simulink and the Control System Toolbox to mechatronic tasks and to evaluate and compare it by means of simulation
  • discuss, interpret and optimize the static and dynamic behavior of a state space controller

Course contents

  • advanced control theory
  • state space control
  • state observers, Kalman filter
  • robust control
  • nonlinear control
  • digital control
  • MatLab/ SIMULINK, Control system toolbox
  • examples (e.g., driver assistance systems)

Prerequisites

Fundamental concepts of control theory, sensor systems, and actors (bachelor level). mathematics (bachelor level), basic knowledge of Matlab / Simulink

Literature

  • U. Kramer, Kraftfahrzeugführung, Modelle - Simulation - Regelung, Carl Hanser Verlag München, 2008. ISBN 978-3-446-40671-1
  • H. Lutz, W. Wendt, Taschenbuch der Regelungstechnik: Mit MATLAB und Simulink, Verlag Harri Deutsch, 2007. ISBN 978-3817118076

Assessment methods

  • Final exam, assessment of lab protocols
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Mobile and Service Robotics 1 (BMUS)
English / ILV
3.00
2.00

Course description

This course focus on concepts of probabilistic robotics based on data processing and movement modelling.

Methodology

Lectures focusing methods and mathematical concepts. The exercises implements different algorithms.

Learning outcomes

After passing this course successfully students are able to ...

  • understand math
  • understand concepts of probabilistic robotics
  • write algorithms

Course contents

  • Probabilistic robotics (basics)

Prerequisites

Fundamentals of mobile robotics Sensor technology

Literature

  • Thrun, S.; Burgard, W.; Fox, D.; Probabilistic Robotics, 2006

Assessment methods

  • Exam
  • Moodle-Quiz
  • Exercises
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Module 8 (MOD8bb)
German / kMod
6.00
-
Air- and Hydromechatronics (BAHM)
German / ILV
3.00
2.00

Course description

Fundamentals of time-continuous mathematical modeling and numeric simulation for hydromechanical components using standard software MATLAB/Simulink

Learning outcomes

After passing this course successfully students are able to ...

  • describe the simulation of hydromechanical systems for mechatronic and robotics incl. principles and laws and application
  • derive linear and nonlinear Differential Equations of hydromechatronical Systems
  • program linear and nonlinear Systems of Differential Equations using MATLAB
  • program linear and nonlinear Systems of Differential Equations using MATLAB
  • perform simple fluid and mechanical simulations using standard Software (MATLAB)
  • estimate required processing effort, computer resources and costs for simple simulations

Course contents

  • fundamentals in fluid mechanics, aerodynamics and gasdynamics
  • derive lineare and nonlinear differential equation of motion for mechanical systems
  • derive lineare and nonlinear differential equations for hydromechanical systems
  • linearization of differential equations
  • transformation of n-th-order differential equation to systems of differential equations
  • stability of nonlinear differentlial equation system
  • derive equilibrium states and eigenvalues of nonlinear differential equations
  • numerical solution of nonlinear differential equations including applications
  • applications in MATLAB/Simulink:
  • simulation of mechanical nonlinear equation of motion
  • simulation of hydraulic systems
  • coupling of mechanical and hydraulic dynamical systems

Prerequisites

Basics of mechanics, mathematics, physics

Literature

  • Sauer, T. (2006), Numerical Analysis, Pearson
  • Böswirth, L./ Bschorer, S. (2012), Technische Strömungslehre, Vieweg+Teubner
  • White, F.M., (2008), Fluidynamics, McGraw-Hill
  • Bollhöfer, M./Mehrmann, V. (2004), Numerische Mathematik, Vieweg

Assessment methods

  • Written final exam
  • Simulationproject
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Optomechatronics (BOPT)
German / ILV
3.00
2.00

Course description

The course covers basics of optical systems and applications in the industry

Methodology

The lecture optomechatronics is divided into two parts: -) Frontal lecture -) Applied simulations in Zemax The aim of the frontal lecture is to teach theory which is later in numerical simulations applied to understand the function of optical systems.

Learning outcomes

After passing this course successfully students are able to ...

  • identify, describe and explain the functional principles of different optical systems
  • evaluate the field of application for given optical systems
  • Demonstration of Basic of photonics
  • explain the physical principles of optical systems and devices and to illustrate these principles via typical industrial applications of optical systems

Course contents

  • Demonstration of Nature and Properties of Light and Light Sources
  • Demonstration of Basic Geometrical & Physical Optics
  • Demonstration of Basic of laser technology
  • Application of Optical systems in industry

Prerequisites

Mechatronics, Mathematics, Optics

Literature

  • Bennamoun M./ Mamic G.J., (2002), Object Recognition – Fundamentals and Case Studies, Springer
  • Burkhardt T./ Feinäugle A./ Fericean S./ Forkl A., (2004) Lineare Weg- und Abstandssensoren – Berührungslose Messsysteme für den industriellen Einsatz, moderne Industrie
  • Hügel H./ Graf T., (2009), Laser in der Fertigung – Strahlenquellen, Systeme, Fertigungsverfahren, 2. Auflage, Vieweg und Teubner
  • Löffler-Mang M., (2012), Optische Sensorik – Lasertechnik, Experimente, Light Barriers, Vieweg und Teubner
  • Losurdo M./ Hingerl K., (2013), Ellipsometry at the Nanoscale, Springer
  • Ruge I./ Mader H., (1991), Halbleiter-Technologie, 3. Auflage, Springer
  • Weissler G. A., (2007), Einführung in die industrielle Bildverarbeitung, Franzis
  • Wirsum S., (1990), Optoelektronik – Schalten, Steuern und Übertragen mit Licht, Franzis

Assessment methods

  • written end exam and Zemax simulation in small groups
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Module 9 (MOD9bb)
German / iMod
6.00
-
Mechatronics 2 (BMECH)
German / ILV
6.00
4.00

Course description

he developed mechatronic system (in the course Mechatronics 1) will be optimized, manufactured, programmed and assembled.

Methodology

Integrated Course

Learning outcomes

After passing this course successfully students are able to ...

  • to design mechatronic systems
  • develop complex technical systems or modify them
  • Basic knowledge in sensor technology, computer science, mechanics (kinematics), electrical engineering, signal and image processing and control engineering

Course contents

  • Realization of a mechatronic system
  • Efficient interaction of mechanical, electronic and information processing systems

Prerequisites

Programming, CAD, Matlab, Robot kinematics

Literature

  • H. Czichos, Mechatronik: Grundlagen und Anwendungen technischer Systeme, Wiesbaden: Vieweg+Teubner, 2008.
  • H. Berhold , P. Döring, L. Klüber, S. Nolte und R. Simon, Mechatronik: Grundlagen und Komponenten, Wiesbaden: Vieweg+Teubner Verlag, 2004.
  • H. Bernstein, Grundlagen der Mechatronik, Berlin: VDE Verlag, 2004.
  • W. Roddeck, Einführung in die Mechatronik, Wiesbaden: Vieweg+Teubner Verlag, 2012.
  • M., Husty, A. Karger, H. Sachs, W. Steinhilper, Kinematik und Robotik, Berlin Heidelberg: Springer-Verlag ,1997.
  • C. Woernler, Mehrkörpersysteme: Eine Einführung in die Kinematik und Dynamik von Systemen starrer Körper, Berlin Heidelberg: Springer-Verlag , 2011.
  • L. Sciavicco, B. Siciliano, Modelling and Control of Robot Manipulators, London: Springer-Verlag, 2000.
  • P. Corke, Robotics Vision and Control: Fundamental Algorithms in MATLAB, Berlin Heidelberg: Springer-Verlag, 2011.

Assessment methods

  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

3. Semester

Name ECTS
SWS
Module 11 Production Management (MOD11bb)
German / kMod
6.00
-
Processmanagement and production planning (BPZM)
German / ILV
4.50
3.00
Service-Oriented and Object-Oriented Algorithms in Robotics (BSOA)
German / ILV
1.50
1.00
Module 12 (MOD12bb)
German / kMod
7.00
-
Industrial Handling (BIHA)
German / ILV
3.00
2.00

Course description

The course provides an overview about industrial handling with a specific emphasis on manipulators and assembly automation

Methodology

LecturesExercisesCalculationsProblem discussion

Learning outcomes

After passing this course successfully students are able to ...

  • describe, analyse and explain handling technology solutions and applications
  • describe the functionality and the application of manipulator technology, beam-engines and automated assembly machines including practical examples
  • explain the functionality of cam and cam indexing mechanisms

Course contents

  • select problems of the conventional handling technology
  • construction and use of manually controlled manipulators
  • Handling in the assembly
  • machine concatenation
  • tape feed
  • motion design
  • Cam and Cam Indexing Mechanisms
  • intelligent handling machines

Prerequisites

Fundamental knowledge in mechanics and handling technologies

Literature

  • Malisa V./ Hesse, S. (2010), Taschenbuch Robotik-Montage-Handhabung; Fachbuchverlag Leipzig im Hanser Verlag
  • Hesse, S., et al., (2001), Manipulatorpraxis, Vieweg, Wiesbaden
  • Hesse, S. (2008), Handhabungstechnik von A bis Z. Hoppenstedt, Darmstadt
  • Volmer, J., Hrsg., (1989), Kurvengetriebe. Verlag Technik, Berlin

Assessment methods

  • written final exam
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Mobile and service robotics 2 (BMUS2)
German / ILV
4.00
2.50

Course description

The course covers the design and basic methods of mobile and service robotics, including first applications; in particular, in-depth discussion of state-of-the-art in a sub-field of service robotics (focus on perception)

Methodology

Slides2 seminar worksWeekly examples to calculate or implement

Learning outcomes

After passing this course successfully students are able to ...

  • understand and implement basic concepts of computer vision, e.g., edge detection,
  • analyse methods of object recognition, implement one of these methods and test it thoroughly in an application scenario, and
  • plan methods of perception for robot applications, discuss advantages and disadvantages, and implement the approach.

Course contents

  • robots as systems
  • service robots and perception (infrared, sonar, laser, vision based sensors)
  • project work in the area of service robotics
  • computer vision, filter, edge detection, colour models, geometric features, interest points, basic algorithms and their implementation
  • object recognition, 3D cameras and 3D data processing
  • cognitive robotics
  • applications for mobile and service robots
  • focus on object recognition and vision for robotics

Prerequisites

- basic knowledge mobile robotics (bachelor level)- course mobile and service robotics 1, Sensors

Literature

  • Szeliski, R., (2010), Computer Vision: Algorithms and Applications, Springer

Assessment methods

  • Weekly Projects
  • Seminars
  • Examination
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Module 13 (MOD13bb)
German / kMod
6.00
-
Agile development methods in the innovation cycle (BINN)
German / ILV
3.00
2.00

Course description

Introduction to the principles of Innovation and Technology Management, and its practice Introduction in agile developmentmethods and their application

Methodology

Lecture and exercise

Learning outcomes

After passing this course successfully students are able to ...

  • understand innovation in its economic and societal dimension.
  • look at innovation from a systemic view, from the vision to the implementation of innovation.
  • evaluate innovation strategies.
  • identify different ways of technology transfer, including concepts such as Open Innovation.
  • deal with the issue of IPR.
  • be aware of the most important research funding programmes in Austria.
  • know what innovative business models are.
  • estimate which role digitalisation plays in R&D.
  • create new ideas, visions.

Course contents

  • Definitions
  • Concepts
  • Interrelations/models
  • Practice - by doing exercises
  • Learning from the best - by cases
  • Reflections

Prerequisites

Basics of Economics and General Management

Literature

  • Hauschildt, J., Salomo, S., Schultz, C., Kock, A. (2016): Innovationsmanagement, Vahlen Verlag.
  • Vahs, D., Brem, A. (2015): Innovationsmanagement: Von der Idee bis zur erfolgreichen Vermarktung, Schäffer-Poeschel Verlag
  • Gassmann, O., Frankenberger, K., Csik, M. (2013): Geschäftsmodelle entwickeln, Hanser Verlag
  • Selected papers

Assessment methods

  • Final written exam, working on cases, doing exercise, as well as participation in the course work
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

Anmerkungen

Interactive work and discussion is the leading principle of the course.

Technical English (BENG)
English / SE
3.00
2.00

Course description

The students acquire skills required for their master’s studies including writing scientific abstracts and papers, and techniques for successful project presentations in English

Methodology

Seminar

Learning outcomes

After passing this course successfully students are able to ...

  • write abstracts and scientific papers in English in compliance with given formal and language-related guidelines
  • present and defend technical projects and papers such as the master’s thesis in English before an exam committee;

Course contents

  • English for scientific writing
  • Structure and language of an English abstract
  • Writing an abstract for the 3rd semester project
  • Structure of a technical project presentation
  • Presentation techniques and relevant language
  • Presentation of the 3rd semester project

Literature

  • Göschka, M. et al (2014) Guidelines for Scientific Writing
  • Handouts on current themes and topics

Assessment methods

  • You will be assessed on the quality of your oral presentation and written abstract, and on your participation in class discussions.
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Module 14 (MOD14bb)
German / iMod
6.00
-
Project (PRJ)
German / PRJ
6.00
4.00

Course description

Self-dependent execution and solving of an individual project task in the field of mechatronics/ robotics

Methodology

Project work

Learning outcomes

After passing this course successfully students are able to ...

  • define a project based on a rough conceptual assignment
  • develop a concrete phase approach for the assigned project based on generic system engineering models; according to the assigned topic this may also include (rapid) prototyping methods
  • create a project plan (Gantt-chart and project structure plan (PSP)) with regard to the dimensions of time, financial requirements and resource allocation
  • perform a feasibility evaluation according to project progress (if applicable twice – once after having finished the logical system model, and a second time after having completed the physical system design); this includes the modification of the initial Gantt-chart and PSP according to the findings of these feasibility considerations
  • implement the respective project starting with kick-off via customer requirement analysis and 1-2 progress report(s)/ -presentation(s) up to technical set-up, final project presentation and technical documentation, within the course timeframe (scheduled project presentations and meetings)

Course contents

  • project definition, requirement specification, feasibility
  • kick-off
  • project planning und -execution
  • functional model
  • system design
  • prototype development
  • progress reports, final presentation
  • writing of a scientific project report

Prerequisites

- basic knowledge scientific writing- project management- technical knowledge in the field of mechatronics/ robotics (depending on project topic)

Literature

  • Anglia Ruskin University, (2010), Guide to the Harvard Style of Referencing, 2nd edition
  • Teschl S., Göschka, K.M., (2010), Leitfaden zur Verfassung einer Bachelorarbeit oder Master Thesis, Version 3.0 und Institut für Mechatronics, Änderungen zum Leitfaden, V4, August 2011
  • Skern, T., (2009), Writing Scientific English: A Workbook, UTB, Stuttgart
  • further topic-specific literature

Assessment methods

  • Assessment of the results of the project and of the project report.
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Module 15 (MOD15bb)
German / kMod
5.00
-
Business Management (BUNF)
German / ILV
3.00
2.00

Course description

Operating as well as strategic business. Business Case: The most obvious reason for developing a business case is to justify the resources and capital investment necessary to bring the project or investment to fruition. In this part of the course the students focus on how to write a business case.

Methodology

LectureDiscussionExamples studentes will create a Business Case, lecturer will coach

Learning outcomes

After passing this course successfully students are able to ...

  • interpret business key data
  • develop a business case

Course contents

  • Business Case:
  • purpose, applicability and limitations of a business case
  • cost-benefit analysis
  • investment calculation
  • financing
  • operating and strategic business

Prerequisites

Economic basics: - accounting- investment budgeting- financing

Literature

  • Malik, F. (2014), Führen, leisten, Leben: Wirksames Management für eine neue Welt, campus

Assessment methods

  • business case (30%)Written exam (70%)
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.
Taxation Law (BSTR)
German / ILV
2.00
1.50

Course description

main features of austrian taxes and social insurance law

Methodology

lecture

Learning outcomes

After passing this course successfully students are able to ...

  • categorize their income according to the referable type of taxation
  • determine their annual earnings and calculate the corresponding taxes and social insurance
  • determine a potential value added tax liability
  • differentiate between several kinds of employment, evaluate respective advantages and disadvantages and calculate the corresponding costs

Course contents

  • income tax
  • corporate income tax
  • tax on sales/ purchases
  • types of employment contracts with regard to social insurance

Prerequisites

basics of company law

Literature

  • Lebensaft-Melwisch G./ Lebensaft G., (2017), Überblick Steuerrecht und Sozialversicherung,

Assessment methods

  • Final written exam, in-class particiption
  • When a group mark is given that is identical for all team members, the lecturer and the degree programme director reserve the right to give different individual marks in cases where there is a noticeable discrepancy in the level of achievement of individual students.

4. Semester

Name ECTS
SWS
Module 16 (MOD16)
German / kMod
30.00
-
Master's Thesis (MTbb)
German / BE
27.00
0.00

Learning outcomes

After passing this course successfully students are able to ...

  • prepare a master thesis
  • compile and present the result in different dissemination format (especially 2-sided extended abstract, scientific poster, where applicable by means of demonstrating objects, devices or mechanisms or a short exemplarily video demonstration
  • present a master thesis in German or English language (defensio)
Seminar for Master´s Thesis (DISbb)
German / SE
3.00
2.00

Course description

The course covers main principles and methods with relevance for academic thesis writing in the area of engineering, in particular on master thesis level. Furthermore, the course supports master thesis candidates as an additional support supplementary to the supervising master thesis tutors.

Learning outcomes

After passing this course successfully students are able to ...

  • define a master thesis, in particular - identify and describe a relevant research gap - define research questions on master thesis level - develop a scientifically sound research design
  • further enhance this thesis outline into a 3-5page disposition based on a literature review regarding the state of the art of the chosen research field
  • continue this thesis disposition toward a finalized master thesis
  • present the master thesis in the final oral master exam

Course contents

  • research gap, research question, research objectives
  • research design, scientific methodology and methods
  • chain of arguments within a master thesis
  • thesis presentation, peer-feedback
  • conflicts and difficult situations in leading project teams
  • individual support, questions & answers

Literature

  • depending on specific topic

Assessment methods

  • Assessment at end of course