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Explanatory Notes
Instructions
Web page:
https://cw.fel.cvut.cz/wiki/courses/aro
Anotation:
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.
Content:
https://cw.fel.cvut.cz/wiki/courses/aro/start
Course outlines:
https://cw.fel.cvut.cz/wiki/courses/aro/lectures/start
Exercises outline:
https://cw.fel.cvut.cz/wiki/courses/aro/tutorials/start
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 |
Requirements:
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, multidimensional Taylor polynomial), linear algebra (least-squares method), probability theory (multivariate gaussian probability), statistics (maximum likelihood and maximum aposteriori estimate), python programming and machine learning algorithms.
Subject is included into these academic programs:
| Page updated 14.10.2024 17:51:34, semester: Z/2025-6, Z,L/2024-5, L/2023-4, Send comments about the content to the Administrators of the Academic Programs |
Proposal and Realization: I. Halaška (K336), J. Novák (K336) |