Code: BE3M33ARO1 Autonomous Robotics
Lecturer: doc. Ing. Karel Zimmermann Ph.D. Weekly load: 2P+2L Completion: A, EX
Department: 13133 Credits: 6 Semester: S
Description:
The Autonomous robotics course will explain the principles needed to develop algorithms for intelligent mobile robots such as algorithms for:
(1) Mapping and localization (SLAM) sensors calibration (lidar or camera).
(2) Planning the path in the existing map or planning the exploration in a partially unknown map and performing the plan in the world.
IMPORTANT: It is assumed that students of this course have a working knowledge of optimization (Gauss-Newton method, Levenberg Marquardt method, full Newton method), mathematical analysis (gradient, Jacobian, Hessian), linear algebra (least-squares method), probability theory (multivariate gaussian probability), statistics (maximum likelihood and maximum aposteriori estimate), python programming and machine learning algorithms.

This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at https://prg.ai/minor.
Contents:
https://cw.fel.cvut.cz/wiki/courses/aro/lectures/start
Seminar contents:
https://cw.fel.cvut.cz/wiki/courses/aro/tutorials/start
Recommended literature:
1. Siciliano, Bruno and Sciavicco, Lorenzo and Villani, Luigi and Oriolo, Giuseppe: Robotics, Modelling,
Planning and Control, Springer 2009
2. Fahimi, F.: Autonomous Robots: Modeling, Path Planning, and Control, Springer 2009

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