Operating autonomously in unknown and dynamically changing environments is a core challenge that all robotic systems must solve to work successfully in industrial, public, and private areas. Currently popular systems that must demonstrate such capabilities include self-driving cars, autonomously operating drones, and personal robotic assistants. In this course, students obtain deep knowledge in creating autonomous robotic systems that can operate in and manipulate unknown and dynamically changing environments by autonomously planning, analysing, mapping, and modelling of such environments. Students learn to approach these challenging tasks through three main techniques: swarm intelligence, model-based probabilistic frameworks, and (mostly) model-free techniques from artificial evolution and machine learning.
Prerequisites
None.
Desired Prior Knowledge: Discrete Mathematics, Linear Algebra, Probabilities and Statistics, Data Structures and Algorithms, Machine Learning, Search Techniques.
Recommended reading
Floreano and Nolfi (2000), Evolutionary Robotics, The MIT press. ISBN-13: 978-0262640565.
Dario Floreano und Claudio Mattiussi (2008), Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, ISBN-13: 978-0262062718
More information at: https://curriculum.maastrichtuniversity.nl/meta/463161/autonomous-robotic-systems
