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Information processing for robotics
Informationsverarbeitung in der Robotik
Last Updated: 2026-02-05 15:13:52
Abstract
The lecture gives a short introduction into machine learning techniques, Neural Networks, Fuzzy Logic, Genetic Algorithms and combination of these methods (hybrid models).After a short presentation of the theoretical background, the application of these methods in robotics are presented, the possibilities and the limits of these methods are discussed.
Objective
The objective of the course is to give a short introduction into modern information processing methods and their application in robotics. In particular machine learning techniques, Neural Networks, Fuzzy Logic, Genetic Algorithms and combination of these methods (hybrid models) are addressed.
Content
The topics in the course are: 1. Artificial Intelligence (AI) 1.1. Introduction 1.2. History of AI, lessons learned from the beginnings of AI 2. Neural Networks 2.1. Introduction in Neural Networks 2.1. Perceptrons, Multilayer Perceptrons 2.2. Kohonen's Self Organizing Maps and extensions of H. Ritter 2.3. RuleNet 2.4. Application examples in robotics with these models 3. Fuzzy Logic 3.1 Introduction 3.2 Theory and application examples in robotics 4. Neuro-Fuzzy Systems 5. Genetic Algorithms 5.1 Introduction 5.2 Theory and application examples in robotics 5.3 Genetic Programming 5.4 Combination with Neural Networks 6. Machine Learning Techniques
Resources
Lecture Notes
Copies of the slides will be distributed during the course.
Literature
References to papers and books will be presented during the lectures.
General Information
- Language
- German
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 30 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Informationsverarbeitung in der Robotik |
|
3 h weekly |