Integrated (Embedded) Intelligent Systems
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2063 is a semester module in German or English language at Bachelor’s level and Master’s level which is offered in summer semester.
This module description is valid to WS 2017/8.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
The course covers the following topics:
- sensors, effectors, and the physical infrastructure of intelligent embedded systems, in particular smart sensors, sensor networks, computer systems in everyday objects, ...
- computational models for controlling embedded intelligent systems: dynamical system model, rational agent model, technical cognitive systems;
- principles of probabilistic state estimation: Bayes filter, Kalman filter, particle filter, multi hypotheses tracking, learning sensor and action models, Hidden Markov models, expectation maximization;
- applications of probabilistic state estimation: self localization, acquisition of environment models, object tracking;
- acquisition of motion and action models and interpretation of intensional activities;
- programming methods for intelligent embedded systems: concurrent reactive control, behavior-based programming, plan-based robot control,grid-based programming techniques.
Courses, Learning and Teaching Methods and Literature
Learning and Teaching Methods
Roland Siegwart, Illah R. Nourbakhsh: Introduction to Autonomous Mobile Robots (Intelligent Robotics and Autonomous Agents), The MIT Press