Cognitive robot systems use sensors and cameras to perceive their environment, in order to acquire and process knowledge for goal directed behavioral decisions. Such systems can be robot vehicles (e.g. for map buildung), robot arms (e.g. for object grasping), or robot heads (e.g. for active vision). The main focus of the course is on methods to reach such intelligent robot behaviors. This includes architectures, space representation, self localisation, navigation, visual servoing, online robot learning, robotics simulation. In the practical part, selected topics are being deepened using mobile robots and the Robot Operating System in programming language Python. Contents at a glance:
- Applications of Cognitive Robot Systems
- Cognitive perception-action systems
- Components of robot systems
- Sensor components as basis for autonomy
- Coordinate systems and transformations
- Visual Servoing of a robot arm
- Representation of environment
- Robot motion planning
- Probabilistic robot localisation
- Online robot learning for navigation
- Robotics simulation
- Programming of cognitive robot systems
- Robot Operating System
Students should get to know possible architectures of cognitive robot systems. They should understand selected methods to solve motion planning and robot navigation, self localisation and obstacle avoidance, and should be familiar with the basic mathematics. For selected problems, they should be able to propose and evaluate potential configurations for cognitive robot systems.
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- H. Choset, at al.: Principles of Robot Motion, MIT Press, 2005.
- J. Latombe: Robot Motion Planning, Kluwer Academic Publishers, 1991.
- S. Niku: Introduction to Robotics, Prentice Hall, 2001.
- B. Siciliano, O. Khatib: Handbook of Robotics, Springer, 2008.
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