Summer Semester

Das Praktikum befasst sich mit der automatisierten Erfassung und Verarbeitung von Sensor- und Messsignalen. Da es sich um Versuche aus den Gebieten Sensorik mechanischer Größen, Messsignalverarbeitung und Programmierung von Messplätzen handelt, werden hauptsächlich Studenten aus den Studienrichtungen EEI und Mechatronik, aber auch MB und CE angesprochen.

Die Versuche umfassen im Einzelnen die Themen

• Grundlagen zur graphischen Programmierung mit NI LabVIEW, Generierung und Erfassung von Signalen, virtuelles Oszilloskop, virtueller Funktionsgenerator

• Messung von Winkel und Drehzahl (Einsatz unterschiedlicher Messprinzipien)

• Charakterisierung von Störungen und Rauschen, Analyse im Zeit- und Frequenzbereich, Filterung von Messsignalen
• Was ist „Echtzeit”? Synchronisation von Abläufen, Einsatz von FPGAs

Im Praktikum Grundlagen der Elektrotechnik III sollen die theoretisch erlangten Kenntnisse der Vorlesung durch praktische Versuche vertieft werden. Im Laufe des Semesters werden die Studenten vier Laborversuche durchführen:

• Versuch 1: Einschaltvorgänge
• Versuch 2: Operationsverstärker
• Versuch 3: Brückenschaltung
• Versuch 4: Nichtlineare Bauteile

Von den Studenten wird verlangt sich intensiv auf die Versuche vorzubereiten um den größten Lernerfolg zu erreichen.

Das Praktikum gilt als bestanden, wenn alle vier Versuche erfolgreich durchgeführt wurden.

Courses:

Human-centred Mechatronics and Robotics (2 SWS, Philipp Beckerle).

Exercises in Human-centred Mechatronics and Robotics (Exercise, 2 SWS, Adna Bliek)

Content:

• Human-oriented design methods
• Biomechanics
  • Motions, measurement, and analysis
  • Biomechanical models
• Elastic robotics
  • Elastic actuators

• Cognitive and physical human-robot interaction
• Empirical research methods
  • Research process and experiment design

• System integration and fault treatment

The exercise will combine simulation sessions and a flip-the-classroom seminar where student groups present recent research papers and discuss them with all attendees.

Learning objectives and competencies:

On successful completion of this module, students will be able to:

• Tackle the interdisciplinary challenges of human-centered robot design.
• Use engineering methods for modeling, design, and control to develop human-centered robots.
• Apply methods from psychology (perception, experience), biomechanics (motion and human models), and engineering (design methodology) and interpret their results.
• Develop robotic systems that provide user-oriented interaction characteristics in addition to efficient and reliable operation.

Courses:

Mechatronic Components and Systems (Lecture, 2 SWS, Philipp Beckerle)

Exercises in Mechatronic Components and Systems (Excersices, 2 SWS, Rodrigo J. Velasco Guillen)

Content:

• System thinking and integration
  • Interactions of hardware and software
  • Engineering design methods
• Mechanical components
• Actuators
• Sensors for measuring mechanical quantities
• Control and information processing

Learning objectives and competencies:

On successful completion of this module, students will be able to:

• Holistically understand mechatronic systems and optimize them using methods of system integration, control, and information processing.
• Grundlegende mechanische Komponenten unterscheiden, charakterisieren, modellieren und im Rahmen des Systementwurfs auswählen und dimensionieren.
• Distinguish, characterize, model, and select basic mechanical components to dimension them in terms of system design.
• Describe electrodynamic, electromagnetic, fluid power, and unconventional actuators phenomenologically and mathematically to dimension them considering the overall system.
• Describe sensors for measuring mechanical quantities phenomenologically and mathematically and dimension them taking into account the overall system.

Courses: 

Learning Interfaces for Autonomous Systems (SS 2022, Lecture, 2.5 SWS, Anany Dwivedi). 

Content: 

• Human-machine interfaces 
• Signal conditioning and feature extraction 

• Machine learning 
• Sensor fusion 
• Applications of learning interfaces for autonomous systems 
• Hands-on sessions: data acquisition using electromyography sensor, signal processing and classification, applications in unity. 

Learning objectives and competencies: 

On successful completion of this module, students will be able to: 

• Distinguish basic components of a human-machine interface and select them appropriately in interface design  

• Tackle the interdisciplinary challenges of human-machine interfaces. 

• Understand concepts of machine learning (classification and regression). 
• Utilize the learned knowledge to design and implement human-machine interfaces. 

Course:

Seminar Autonomous Systems and Mechatronics (Seminar, 2 SWS, compulsory attendance, Anany Dwivedi, Mehmet Ege Cansev, Philipp Beckerle)

Content:

In the seminar, students will analyze, present. and discuss recent research topics in autonomous systems and mechatronics. This will comprise mechatronic component, system, and control design as well as advanced methods aiming at autonomous operation. Besides reflecting contemporary literature, the students are asked to conclude and suggest directions for future research.

Learning objectives and competencies:

On successful completion of this module, students will be able to comprehend and convey recent research challenges in the area of autonomous system and mechatronics. Moreover, they are prepared to infer future research lines from recent developments.

Course:

Seminar Cognitive Science in Engineering  (Seminar, 2 SWS, compulsory attendance, Stella Hao, Philipp Beckerle )

Content:

In the seminar, students will analyze, present. and discuss recent research topics in Cognitve Science and Engineering. Besides reflecting contemporary literature, the students are asked to conclude and suggest directions for future research.

Learning objectives and competencies:

On successful completion of this module, students will be able to comprehend and convey recent research challenges in the area of Cognitive Science in Engineering. Moreover, they are prepared to infer future research lines from recent developments.