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Autonomous Robotic Systems

Full course description

Autonomous Systems introduces the students to the foundation of situated autonomous robots. The course will start with an introduction to the field of mobile robots. Concepts such as recursive state estimation and Kalman filtering will be introduced. Additionally basic questions of how to effectively control a mobile robot will be addressed. The core of this course will address the problems of localization, planning and control, perception and robot motion and navigation. Planning and control will be approached from a probabilistic perspective in the form of Markov Decision Processes, and Partial Observable Markov Decision processes. Robot navigation on the other hand will be approached from an evolutionary perspective where the focus will be on swarm intelligence, genetic algorithms, and neural networks. This course will be accompanied by a large practical part in which students have the opportunity to apply the learned material in practice. After completing this course, students will have a good understanding of the major concepts in autonomous systems such as localization, planning and control. The student will be able to apply the learned concepts to real autonomous systems.


Discrete Mathematics, Linear Algebra, Probabilities and Statistics, Data Structures and Algorithms

Some papers to be announced at Eleum.


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study/master_ai/year_1/block_4/autonomous_robotic_systems.txt · Last modified: 2021/07/04 14:20 by nicolasperez